Prospective meta-analysis of interleukin 1 gene complex polymorphisms confirms associations with ankylosing spondylitis

Objectives: The aim of the current study was to determine the contribution of interleukin (IL) 1 gene cluster polymorphisms previously implicated in susceptibility for ankylosing spondylitis (AS) to AS susceptibility in different populations worldwide. Methods: Nine polymorphisms in the IL1 gene cluster members IL1A (rs2856836, rs17561 and rs1894399), IL1B (rs16944), IL1F10 (rs3811058) and IL1RN (rs419598, the IL1RA VNTR, rs315952 and rs315951) were genotyped in 2675 AS cases and 2592 healthy controls recruited in 12 different centres in 10 countries. Association of variants with AS was tested by Mantel-Haenszel random effects analysis. Results: Strong association was observed with three single nucleotide polymorphisms (SNPs) in the IL1A gene (rs2856836, rs17561, rs1894399, p = 0.0036, 0.000019 and 0.0003, respectively). There was no evidence of significant heterogeneity of effects between centres, and no evidence of non-combinability of findings. The population attributable risk fraction of these variants in Caucasians is estimated at 4-6%. Conclusions: This study confirms that IL1A is associated with susceptibility to AS. Association of the other IL1 gene complex members could not be excluded in specific populations. Prospective meta-analysis is a useful tool in confirmation studies of genes associated with complex genetic disorders such as AS, providing sufficiently large sample sizes to produce robust findings often not achieved in smaller individual cohorts.

Is disease severity in ankylosing spondylitis genetically determined?

Objective. To assess the role of genes and the environment in determining the severity of ankylosing spondylitis. Methods: One hundred seventy-three families with >1 case of ankylosing spondylitis were recruited (120 affected sibling pairs, 26 affected parent-child pairs, 20 families with both first- and second-degree relatives affected, and 7 families with only second-degree relatives affected), comprising a total of 384 affected individuals. Disease severity was assessed by the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and functional impairment was determined using the Bath Ankylosing Spondylitis Functional Index (BASFI). Disease duration and age at onset were also studied. Variance-components modeling was used to determine the genetic and environmental components Contributing to familiality of the traits examined, and complex segregation analysis was performed to assess different disease models. Results. Both the disease activity and functional capacity as assessed by the BASDAI and the BASFI, respectively, were found to be highly familial (BASDAI familiality 0.51 [P = 10-4], BASFI familiality 0,68 [P = 3 × 10-7]). No significant shared environmental component was demonstrated to be associated with either the BASDAI or the BASFI. Including age at disease onset and duration of disease as covariates made no difference in the heritability assessments. A strong correlation was noted between the BASDAI and the BASFI (genetic correlation 0.9), suggesting the presence of shared determinants of these 2 measures. However, there was significant residual heritability for each measure independent of the other (BASFI residual heritability 0.48, BASDAI 0,36), perhaps indicating that not all genes influencing disease activity influence chronicity. No significant heritability of age at disease onset was found (heritability 0.18; P = 0.2). Segregation studies suggested the presence of a single major gene influencing the BASDAI and the BASFI. Conclusion. This study demonstrates a major genetic contribution to disease severity in ankylosing spondylitis. As with susceptibility to ankylosing spondylitis, shared environmental factors play little role in determining the disease severity.

Dissection of class III major histocompatibility complex haplotypes associated with rheumatoid arthritis

Objective. We have previously identified a single-nucleotide polymorphism (SNP) haplotype involving the lymphotoxin α (LTA) and tumor necrosis factor (TNF) loci (termed haplotype LTA-TNF2) on chromosome 6 that shows differential association with rheumatoid arthritis (RA) on HLA-DRB1*0404 and *0401 haplotypes, suggesting the presence of additional non-HLA-DRB1 RA susceptibility genes on these haplotypes. To refine this association, we performed a case-control association study using both SNPs and microsatellite markers in haplotypes matched either for HLA-DRB1*0404 or for HLA-DRB1*0401. Methods. Fourteen SNPs lying between HLA-DRB1 and LTA were genotyped in 87 DRB1*04-positive families. High-density microsatellite typing was performed using 24 markers spanning 2,500 kb centered around the TNF gene in 305 DRB1*0401 or *0404 cases and 400 DRB1*0401 or *0404 controls. Single-marker, 2-marker, and 3-marker minihaplotypes were constructed and their frequencies compared between the DRB1*0401 and DRB1*0404 matched case and control haplotypes. Results. Marked preservation of major histocompatibility complex haplotypes was seen, with chromosomes carrying LTA-TNF2 and either DRB1*0401 or DRB1*0404 both carrying an identical SNP haplotype across the 1-Mb region between TNF and HLA-DRB1. Using microsatellite markers, we observed two 3-marker minihaplotypes that were significantly overrepresented in the DRB1*0404 case haplotypes (P = 0.00024 and P = 0.00097). Conclusion. The presence of a single extended SNP haplotype between LTA-TNF2 and both DRB1*0401 and DRB1*0404 is evidence against this region harboring the genetic effects in linkage disequillbrium with LTA-TNF2. Two RA-associated haplotypes on the background of DRB1*0404 were identified in a 126-kb region surrounding and centromeric to the TNF locus.

Non-major-histocompatibility-complex genetics of ankylosing spondylitis

There is strong evidence from twin and family studies indicating that a substantial proportion of the heritability of susceptibility to ankylosing spondylitis (AS) and its clinical manifestations is encoded by non-major-histocompatibility-complex genes. Efforts to identify these genes have included genomewide linkage studies and candidate gene association studies. One region, the interleukin (IL)-1 gene complex on chromosome 2, has been repeatedly associated with AS in both Caucasians and Asians. It is likely that more than one gene in this complex is involved in AS, with the strongest evidence to date implicating IL-1A. Identifying the genes underlying other linkage regions has been difficult due to the lack of obvious candidates and the low power of most studies to date to identify genes of the small to moderate magnitude that are likely to be involved. The field is moving towards genomewide association analysis, involving much larger datasets of unrelated cases and controls. Early successes using this approach in other diseases indicates that it is likely to identify genes in common diseases like AS, but there remains the risk that the common-variant, common-disease hypothesis will not hold true in AS. Nonetheless, it is appropriate for the field to be cautiously optimistic that the next few years will bring great advances in our understanding of the genetics of this condition.

Allergen capture by IgE and IgG enhanced cell-binding by allergen multimers: How complex is it?

Allergic diseases are the most common chronic disease of the western world, costing $7.8 billion per year in lost productivity and medical care in Australia alone.1 IgE is central to the immunopathogenesis of allergic diseases and important advances are now being made on multiple fronts of IgE research. In particular, two groups independently invested in the generation of IgE reporter mice to address the vexing question of the route of development of the elusive IgE+ B cell.2, 3 Two new anti-IgE mAb targeting membrane IgE and cell-bound IgE have the potential to deplete the cellular source of IgE.4, 5 These could be candidates for alternative anti-IgE treatment options with advantages over current anti-IgE therapy (OmalizumAb), which depletes free serum IgE. Researchers are still intrigued by the modes of interaction of IgE with allergen, and with both its receptors; the high affinity FcεR1 on mast cells and basophils, and the low affinity, C-type lectin, IgE receptor, CD23,6 on B cells and monocytes (Figure 1a and b). A new approach to the study of the complexity of these interactions was recently reported by Reginald et al.7 on page 167 of this issue.

Analysis of complex molecular systems: The impact of multivariate analysis for resolving the interactions of small molecules with biopolymers - a review

This review is focused on the impact of chemometrics for resolving data sets collected from investigations of the interactions of small molecules with biopolymers. These samples have been analyzed with various instrumental techniques, such as fluorescence, ultraviolet–visible spectroscopy, and voltammetry. The impact of two powerful and demonstrably useful multivariate methods for resolution of complex data—multivariate curve resolution–alternating least squares (MCR–ALS) and parallel factor analysis (PARAFAC)—is highlighted through analysis of applications involving the interactions of small molecules with the biopolymers, serum albumin, and deoxyribonucleic acid. The outcomes illustrated that significant information extracted by the chemometric methods was unattainable by simple, univariate data analysis. In addition, although the techniques used to collect data were confined to ultraviolet–visible spectroscopy, fluorescence spectroscopy, circular dichroism, and voltammetry, data profiles produced by other techniques may also be processed. Topics considered including binding sites and modes, cooperative and competitive small molecule binding, kinetics, and thermodynamics of ligand binding, and the folding and unfolding of biopolymers. Applications of the MCR–ALS and PARAFAC methods reviewed were primarily published between 2008 and 2013.

Energy gap in the aetiology of body weight gain and obesity: A challenging concept with a complex evaluation and pitfalls

The concept of energy gap(s) is useful for understanding the consequence of a small daily, weekly, or monthly positive energy balance and the inconspicuous shift in weight gain ultimately leading to overweight and obesity. Energy gap is a dynamic concept: an initial positive energy gap incurred via an increase in energy intake (or a decrease in physical activity) is not constant, may fade out with time if the initial conditions are maintained, and depends on the 'efficiency' with which the readjustment of the energy imbalance gap occurs with time. The metabolic response to an energy imbalance gap and the magnitude of the energy gap(s) can be estimated by at least two methods, i.e. i) assessment by longitudinal overfeeding studies, imposing (by design) an initial positive energy imbalance gap; ii) retrospective assessment based on epidemiological surveys, whereby the accumulated endogenous energy storage per unit of time is calculated from the change in body weight and body composition. In order to illustrate the difficulty of accurately assessing an energy gap we have used, as an illustrative example, a recent epidemiological study which tracked changes in total energy intake (estimated by gross food availability) and body weight over 3 decades in the US, combined with total energy expenditure prediction from body weight using doubly labelled water data. At the population level, the study attempted to assess the cause of the energy gap purported to be entirely due to increased food intake. Based on an estimate of change in energy intake judged to be more reliable (i.e. in the same study population) and together with calculations of simple energetic indices, our analysis suggests that conclusions about the fundamental causes of obesity development in a population (excess intake vs. low physical activity or both) is clouded by a high level of uncertainty.

Low delta18O zircon grains in the Neoarchean Rum Jungle Complex, northern Australia: An indicator of emergent continental crust

The timing of widespread continental emergence is generally considered to have had a dramatic effect on the hydrological cycle, atmospheric conditions, and climate. New secondary ion mass spectrometry (SIMS) oxygen and laser-ablation–multicollector–inductively coupled plasma–mass spectrometry (LA-MC-ICP-MS) Lu-Hf isotopic results from dated zircon grains in the granitic Neoarchean Rum Jungle Complex provide a minimum time constraint on the emergence of continental crust above sea level for the North Australian craton. A 2535 ± 7 Ma monzogranite is characterized by magmatic zircon with slightly elevated δ18O (6.0‰–7.5‰ relative to Vienna standard mean ocean water [VSMOW]), consistent with some contribution to the magma from reworked supracrustal material. A supracrustal contribution to magma genesis is supported by the presence of metasedimentary rock enclaves, a large population of inherited zircon grains, and subchondritic zircon Hf (εHf = −6.6 to −4.1). A separate, distinct crustal source to the same magma is indicated by inherited zircon grains that are dominated by low δ18O values (2.5‰–4.8‰, n = 9 of 15) across a range of ages (3536–2598 Ma; εHf = −18.2 to +0.4). The low δ18O grains may be the product of one of two processes: (1) grain-scale diffusion of oxygen in zircon by exchange with a low δ18O magma or (2) several episodes of magmatic reworking of a Mesoarchean or older low δ18O source. Both scenarios require shallow crustal magmatism in emergent crust, to allow interaction with rocks altered by hydrothermal meteoric water in order to generate the low δ18O zircon. In the first scenario, assimilation of these altered rocks during Neoarchean magmatism generated low δ18O magma with which residual detrital zircons were able to exchange oxygen, while preserving their U-Pb systematics. In the second scenario, wholesale melting of the altered rocks occurred in several distinct events through the Mesoarchean, generating low δ18O magma from which zircon crystallized. Ultimately, in either scenario, the low δ18O zircons were entrained as inherited grains in a Neoarchean granite. The data suggest operation of a modern hydrological cycle by the Neoarchean and add to evidence for the increased emergence of continents by this time

Advanced tissue engineering scaffold design for regeneration of the complex hierarchical periodontal structure

AIM: This study investigated the ability of an osteoconductive biphasic scaffold to simultaneously regenerate alveolar bone, periodontal ligament and cementum. MATERIALS AND METHODS: A biphasic scaffold was built by attaching a fused deposition modelled bone compartment to a melt electrospun periodontal compartment. The bone compartment was coated with a calcium phosphate (CaP) layer for increasing osteoconductivity, seeded with osteoblasts and cultured in vitro for 6 weeks. The resulting constructs were then complemented with the placement of PDL cell sheets on the periodontal compartment, attached to a dentin block and subcutaneously implanted into athymic rats for 8 weeks. Scanning electron microscopy, X-ray diffraction, alkaline phosphatase and DNA content quantification, confocal laser microscopy, micro computerized tomography and histological analysis were employed to evaluate the scaffold's performance. RESULTS: The in vitro study showed that alkaline phosphatase activity was significantly increased in the CaP-coated samples and they also displayed enhanced mineralization. In the in vivo study, significantly more bone formation was observed in the coated scaffolds. Histological analysis revealed that the large pore size of the periodontal compartment permitted vascularization of the cell sheets, and periodontal attachment was achieved at the dentin interface. CONCLUSIONS: This work demonstrates that the combination of cell sheet technology together with an osteoconductive biphasic scaffold could be utilized to address the limitations of current periodontal regeneration techniques.

Droplet digital PCR as a useful tool for the quantitative detection of Enterovirus 71

Hand, foot and mouth disease (HFMD) is a contagious viral disease that frequently affects infants and children and present with blisters and flu-like symptoms. This disease is caused by a group of enteroviruses such as enterovirus 71 (EV71) and coxsackievirus A16 (CA16). However, unlike other HFMD causing enteroviruses, EV71 have also been shown to be associated with more severe clinical manifestation such as aseptic meningitis, brainstem and cerebellar encephalitis which may lead to cardiopulmonary failure and death. Clinically, HFMD caused by EV71 is indistinguishable from other HFMD causing enteroviruses such as CA16. Molecular diagnosis methods such as the use of real-time PCR has been used commonly for the identification of EV71. In this study, two platforms namely the real-time PCR and the droplet digital PCR were compared for the detection quantitation of known EV71 viral copy number. The results reveal accurate and consistent results between the two platforms. In summary, the droplet digital PCR was demonstrated to be a promising technology for the identification and quantitation of EV71 viral copy number.

Combining HPLC-DAD and ICP-MS data for improved analysis of complex samples: Classification of the root samples from Cortex moutan

A combined data matrix consisting of high performance liquid chromatography–diode array detector (HPLC–DAD) and inductively coupled plasma-mass spectrometry (ICP-MS) measurements of samples from the plant roots of the Cortex moutan (CM), produced much better classification and prediction results in comparison with those obtained from either of the individual data sets. The HPLC peaks (organic components) of the CM samples, and the ICP-MS measurements (trace metal elements) were investigated with the use of principal component analysis (PCA) and the linear discriminant analysis (LDA) methods of data analysis; essentially, qualitative results suggested that discrimination of the CM samples from three different provinces was possible with the combined matrix producing best results. Another three methods, K-nearest neighbor (KNN), back-propagation artificial neural network (BP-ANN) and least squares support vector machines (LS-SVM) were applied for the classification and prediction of the samples. Again, the combined data matrix analyzed by the KNN method produced best results (100% correct; prediction set data). Additionally, multiple linear regression (MLR) was utilized to explore any relationship between the organic constituents and the metal elements of the CM samples; the extracted linear regression equations showed that the essential metals as well as some metallic pollutants were related to the organic compounds on the basis of their concentrations

Fractal and complex network analyses of protein molecular dynamics

Based on protein molecular dynamics, we investigate the fractal properties of energy, pressure and volume time series using the multifractal detrended fluctuation analysis (MF-DFA) and the topological and fractal properties of their converted horizontal visibility graphs (HVGs). The energy parameters of protein dynamics we considered are bonded potential, angle potential, dihedral potential, improper potential, kinetic energy, Van der Waals potential, electrostatic potential, total energy and potential energy. The shape of the h(q)h(q) curves from MF-DFA indicates that these time series are multifractal. The numerical values of the exponent h(2)h(2) of MF-DFA show that the series of total energy and potential energy are non-stationary and anti-persistent; the other time series are stationary and persistent apart from series of pressure (with H≈0.5H≈0.5 indicating the absence of long-range correlation). The degree distributions of their converted HVGs show that these networks are exponential. The results of fractal analysis show that fractality exists in these converted HVGs. For each energy, pressure or volume parameter, it is found that the values of h(2)h(2) of MF-DFA on the time series, exponent λλ of the exponential degree distribution and fractal dimension dBdB of their converted HVGs do not change much for different proteins (indicating some universality). We also found that after taking average over all proteins, there is a linear relationship between 〈h(2)〉〈h(2)〉 (from MF-DFA on time series) and 〈dB〉〈dB〉 of the converted HVGs for different energy, pressure and volume.

Combined NIR/MIR analysis: A novel method for the classification of complex substances such as Illicium verum Hook. F. and its adulterants

A novel combined near- and mid-infrared (NIR and MIR) spectroscopic method has been researched and developed for the analysis of complex substances such as the Traditional Chinese Medicine (TCM), Illicium verum Hook. F. (IVHF), and its noxious adulterant, Iuicium lanceolatum A.C. Smith (ILACS). Three types of spectral matrix were submitted for classification with the use of the linear discriminant analysis (LDA) method. The data were pretreated with either the successive projections algorithm (SPA) or the discrete wavelet transform (DWT) method. The SPA method performed somewhat better, principally because it required less spectral features for its pretreatment model. Thus, NIR or MIR matrix as well as the combined NIR/MIR one, were pretreated by the SPA method, and then analysed by LDA. This approach enabled the prediction and classification of the IVHF, ILACS and mixed samples. The MIR spectral data produced somewhat better classification rates than the NIR data. However, the best results were obtained from the combined NIR/MIR data matrix with 95–100% correct classifications for calibration, validation and prediction. Principal component analysis (PCA) of the three types of spectral data supported the results obtained with the LDA classification method.

A quantitative metric to identify critical elements within seafood supply networks

A theoretical basis is required for comparing key features and critical elements in wild fisheries and aquaculture supply chains under a changing climate. Here we develop a new quantitative metric that is analogous to indices used to analyse food-webs and identify key species. The Supply Chain Index (SCI) identifies critical elements as those elements with large throughput rates, as well as greater connectivity. The sum of the scores for a supply chain provides a single metric that roughly captures both the resilience and connectedness of a supply chain. Standardised scores can facilitate cross-comparisons both under current conditions as well as under a changing climate. Identification of key elements along the supply chain may assist in informing adaptation strategies to reduce anticipated future risks posed by climate change. The SCI also provides information on the relative stability of different supply chains based on whether there is a fairly even spread in the individual scores of the top few key elements, compared with a more critical dependence on a few key individual supply chain elements. We use as a case study the Australian southern rock lobster Jasus edwardsii fishery, which is challenged by a number of climate change drivers such as impacts on recruitment and growth due to changes in large-scale and local oceanographic features. The SCI identifies airports, processors and Chinese consumers as the key elements in the lobster supply chain that merit attention to enhance stability and potentially enable growth. We also apply the index to an additional four real-world Australian commercial fishery and two aquaculture industry supply chains to highlight the utility of a systematic method for describing supply chains. Overall, our simple methodological approach to empirically-based supply chain research provides an objective method for comparing the resilience of supply chains and highlighting components that may be critical.

Modeling meat yield based on measurements of body traits in genetically improved giant freshwater prawn (GFP) Macrobrachium rosenbergii

A single-generation dataset consisting of 1,730 records from a selection program for high growth rate in giant freshwater prawn (GFP, Macrobrachium rosenbergii) was used to derive prediction equations for meat weight and meat yield. Models were based on body traits [body weight, total length and abdominal width (AW)] and carcass measurements (tail weight and exoskeleton-off weight). Lengths and width were adjusted for the systematic effects of selection line, male morphotypes and female reproductive status, and for the covariables of age at slaughter within sex and body weight. Body and meat weights adjusted for the same effects (except body weight) were used to calculate meat yield (expressed as percentage of tail weight/body weight and exoskeleton-off weight/body weight). The edible meat weight and yield in this GFP population ranged from 12 to 15 g and 37 to 45 %, respectively. The simple (Pearson) correlation coefficients between body traits (body weight, total length and AW) and meat weight were moderate to very high and positive (0.75–0.94), but the correlations between body traits and meat yield were negative (−0.47 to −0.74). There were strong linear positive relationships between measurements of body traits and meat weight, whereas relationships of body traits with meat yield were moderate and negative. Step-wise multiple regression analysis showed that the best model to predict meat weight included all body traits, with a coefficient of determination (R 2) of 0.99 and a correlation between observed and predicted values of meat weight of 0.99. The corresponding figures for meat yield were 0.91 and 0.95, respectively. Body weight or length was the best predictor of meat weight, explaining 91–94 % of observed variance when it was fitted alone in the model. By contrast, tail width explained a lower proportion (69–82 %) of total variance in the single trait models. It is concluded that in practical breeding programs, improvement of meat weight can be easily made through indirect selection for body trait combinations. The improvement of meat yield, albeit being more difficult, is possible by genetic means, with 91 % of the variation in the trait explained by the body and carcass traits examined in this study.