Genetic and Environmental Contributions to Body Mass Index: Comparative Analysis of Monozygotic Twins, Dizygotic Twins and Same-Age Unrelated Siblings

Background—Earlier studies have established that a substantial percentage of variance in obesity-related phenotypes is explained by genetic components. However, only one study has used both virtual twins (VTs) and biological twins and was able to simultaneously estimate additive genetic, non-additive genetic, shared environmental and unshared environmental components in body mass index (BMI). Our current goal was to re-estimate four components of variance in BMI, applying a more rigorous model to biological and virtual multiples with additional data. Virtual multiples share the same family environment, offering unique opportunities to estimate common environmental influence on phenotypes that cannot be separated from the non-additive genetic component using only biological multiples. Methods—Data included 929 individuals from 164 monozygotic twin pairs, 156 dizygotic twin pairs, five triplet sets, one quadruplet set, 128 VT pairs, two virtual triplet sets and two virtual quadruplet sets. Virtual multiples consist of one biological child (or twins or triplets) plus one same-aged adoptee who are all raised together since infancy. We estimated the additive genetic, non-additive genetic, shared environmental and unshared random components in BMI using a linear mixed model. The analysis was adjusted for age, age2, age3, height, height2, height3, gender and race. Results—Both non-additive genetic and common environmental contributions were significant in our model (P-values \u3c 0.0001). No significant additive genetic contribution was found. In all, 63.6% (95% confidence interval (CI) 51.8–75.3%) of the total variance of BMI was explained by a non-additive genetic component, 25.7% (95% CI 13.8–37.5%) by a common environmental component and the remaining 10.7% by an unshared component. Conclusion—Our results suggest that genetic components play an essential role in BMI and that common environmental factors such as diet or exercise also affect BMI. This conclusion is consistent with our earlier study using a smaller sample and shows the utility of virtual multiples for separating non-additive genetic variance from common environmental variance

Measuring Accuracy of Automated Parsing and Categorization Tools and Processes in Digital Investigations

This work presents a method for the measurement of the accuracy of evidential artifact extraction and categorization tasks in digital forensic investigations. Instead of focusing on the measurement of accuracy and errors in the functions of digital forensic tools, this work proposes the application of information retrieval measurement techniques that allow the incorporation of errors introduced by tools and analysis processes. This method uses a `gold standard' that is the collection of evidential objects determined by a digital investigator from suspect data with an unknown ground truth. This work proposes that the accuracy of tools and investigation processes can be evaluated compared to the derived gold standard using common precision and recall values. Two example case studies are presented showing the measurement of the accuracy of automated analysis tools as compared to an in-depth analysis by an expert. It is shown that such measurement can allow investigators to determine changes in accuracy of their processes over time, and determine if such a change is caused by their tools or knowledge.Comment: 17 pages, 2 appendices, 1 figure, 5th International Conference on Digital Forensics and Cyber Crime; Digital Forensics and Cyber Crime, pp. 147-169, 201

Quantitative Mass Spectrometry Analysis Using PAcIFIC for the Identification of Plasma Diagnostic Biomarkers for Abdominal Aortic Aneurysm

BACKGROUND: Abdominal aortic aneurysm (AAA) is characterized by increased aortic vessel wall diameter (>1.5 times normal) and loss of parallelism. This disease is responsible for 1-4% mortality occurring on rupture in males older than 65 years. Due to its asymptomatic nature, proteomic techniques were used to search for diagnostic biomarkers that might allow surgical intervention under nonlife threatening conditions. METHODOLOGY/PRINCIPAL FINDINGS: Pooled human plasma samples of 17 AAA and 17 control patients were depleted of the most abundant proteins and compared using a data-independent shotgun proteomic strategy, Precursor Acquisition Independent From Ion Count (PAcIFIC), combined with spectral counting and isobaric tandem mass tags. Both quantitative methods collectively identified 80 proteins as statistically differentially abundant between AAA and control patients. Among differentially abundant proteins, a subgroup of 19 was selected according to Gene Ontology classification and implication in AAA for verification by Western blot (WB) in the same 34 individual plasma samples that comprised the pools. From the 19 proteins, 12 were detected by WB. Five of them were verified to be differentially up-regulated in individual plasma of AAA patients: adiponectin, extracellular superoxide dismutase, protein AMBP, kallistatin and carboxypeptidase B2. CONCLUSIONS/SIGNIFICANCE: Plasma depletion of high abundance proteins combined with quantitative PAcIFIC analysis offered an efficient and sensitive tool for the screening of new potential biomarkers of AAA. However, WB analysis to verify the 19 PAcIFIC identified proteins of interest proved inconclusive save for five proteins. We discuss these five in terms of their potential relevance as biological markers for use in AAA screening of population at risk

Identification of Trypanosome Proteins in Plasma from African Sleeping Sickness Patients Infected with T. b. rhodesiense

Control of human African sleeping sickness, caused by subspecies of the protozoan parasite Trypanosoma brucei, is based on preventing transmission by elimination of the tsetse vector and by active diagnostic screening and treatment of infected patients. To identify trypanosome proteins that have potential as biomarkers for detection and monitoring of African sleeping sickness, we have used a ‘deep-mining” proteomics approach to identify trypanosome proteins in human plasma. Abundant human plasma proteins were removed by immunodepletion. Depleted plasma samples were then digested to peptides with trypsin, fractionated by basic reversed phase and each fraction analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). This sample processing and analysis method enabled identification of low levels of trypanosome proteins in pooled plasma from late stage sleeping sickness patients infected with Trypanosoma brucei rhodesiense. A total of 254 trypanosome proteins were confidently identified. Many of the parasite proteins identified were of unknown function, although metabolic enzymes, chaperones, proteases and ubiquitin-related/acting proteins were found. This approach to the identification of conserved, soluble trypanosome proteins in human plasma offers a possible route to improved disease diagnosis and monitoring, since these molecules are potential biomarkers for the development of a new generation of antigen-detection assays. The combined immuno-depletion/mass spectrometric approach can be applied to a variety of infectious diseases for unbiased biomarker identification

ProteinSeq: High-Performance Proteomic Analyses by Proximity Ligation and Next Generation Sequencing

Despite intense interest, methods that provide enhanced sensitivity and specificity in parallel measurements of candidate protein biomarkers in numerous samples have been lacking. We present herein a multiplex proximity ligation assay with readout via realtime PCR or DNA sequencing (ProteinSeq). We demonstrate improved sensitivity over conventional sandwich assays for simultaneous analysis of sets of 35 proteins in 5 µl of blood plasma. Importantly, we observe a minimal tendency to increased background with multiplexing, compared to a sandwich assay, suggesting that higher levels of multiplexing are possible. We used ProteinSeq to analyze proteins in plasma samples from cardiovascular disease (CVD) patient cohorts and matched controls. Three proteins, namely P-selectin, Cystatin-B and Kallikrein-6, were identified as putative diagnostic biomarkers for CVD. The latter two have not been previously reported in the literature and their potential roles must be validated in larger patient cohorts. We conclude that ProteinSeq is promising for screening large numbers of proteins and samples while the technology can provide a much-needed platform for validation of diagnostic markers in biobank samples and in clinical use

Quantitative Mass Spectrometry Evaluation of Human Retinol Binding Protein 4 and Related Variants

Background: Retinol Binding Protein 4 (RBP4) is an exciting new biomarker for the determination of insulin resistance and type 2 diabetes. It is known that circulating RBP4 resides in multiple variants which may provide enhanced clinical utility, but conventional immunoassay methods are blind to such differences. A Mass Spectrometric immunoassay (MSIA) technology that can quantitate total RBP4 as well as individual isoforms may provide an enhanced analysis for this biomarker. Methods: RBP4 was isolated and detected from 0.5 uL of human plasma using MSIA technology, for the simultaneous quantification and differentiation of endogenous human RBP4 and its variants. Results: The linear range of the assay was 7.81–500 ug/mL, and the limit of detection and limit of quantification were 3.36 ug/mL and 6.52 ug/mL, respectively. The intra-assay CVs were determined to be 5.1 % and the inter-assay CVs were 9.6%. The percent recovery of the RBP4-MSIA ranged from 95 – 105%. Method comparison of the RBP4 MSIA vs the Immun Diagnostik ELISA yielded a Passing & Bablok fit of MSIA = 1.056 ELISA – 3.09, while the Cusum linearity p-value was.0.1 and the mean bias determined by the Altman Bland test was 1.2%. Conclusion: The novel RBP4 MSIA provided a fast, accurate and precise quantitative protein measurement as compared to the standard commercially available ELISA. Moreover, this method also allowed for the detection of RBP4 variants that are present in each sample, which may in the future provide a new dimension in the clinical utility of this biomarker

Habitat filtering determines spatial variation of macroinvertebrate community traits in northern headwater streams

Although our knowledge of the spatial distribution of stream organisms has been increasing rapidly in the last decades, there is still little consensus about trait-based variability of macroinvertebrate communities within and between catchments in near-pristine systems. Our aim was to examine the taxonomic and trait based stability vs. variability of stream macroinvertebrates in three high-latitude catchments in Finland. The collected taxa were assigned to unique trait combinations (UTCs) using biological traits. We found that only a single or a highly limited number of taxa formed a single UTC, suggesting a low degree of redundancy. Our analyses revealed significant differences in the environmental conditions of the streams among the three catchments. Linear models, rarefaction curves and beta-diversity measures showed that the catchments differed in both alpha and beta diversity. Taxon- and trait-based multivariate analyses also indicated that the three catchments were significantly different in terms of macroinvertebrate communities. All these findings suggest that habitat filtering, i.e., environmental differences among catchments, determines the variability of macroinvertebrate communities, thereby contributing to the significant biological differences among the catchments. The main implications of our study is that the sensitivity of trait-based analyses to natural environmental variation should be carefully incorporated in the assessment of environmental degradation, and that further studies are needed for a deeper understanding of trait-based community patterns across near-pristine streams

Immunodepletion of high-abundant proteins from acute and chronic wound fluids to elucidate low-abundant regulators in wound healing

<p>Abstract</p> <p>Background</p> <p>The process of wound healing consists of several well distinguishable and finely tuned phases. For most of these phases specific proteins have been characterized, although the underlying mechanisms of regulation are not yet fully understood. It is an open question as to whether deficits in wound healing can be traced back to chronic illnesses such as diabetes mellitus. Previous research efforts in this field focus largely on a restricted set of marker proteins due to the limitations detection by antibodies imposes. For mechanistic purposes the elucidation of differences in acute and chronic wounds can be addressed by a less restricted proteome study. Mass spectrometric (MS) methods, e.g. multi dimensional protein identification technology (MudPIT), are well suitable for this complex theme of interest. The human wound fluid proteome is extremely complex, as is human plasma. Therefore, high-abundant proteins often mask the mass spectrometric detection of lower-abundant ones, which makes a depletion step of such predominant proteins inevitable.</p> <p>Findings</p> <p>In this study a commercially available immunodepletion kit was evaluated for the detection of low-abundant proteins from wound fluids. The dynamic range of the entire workflow was significantly increased to 5-6 orders of magnitude, which makes low-abundant regulatory proteins involved in wound healing accessible for MS detection.</p> <p>Conclusion</p> <p>The depletion of abundant proteins is absolutely necessary in order to analyze highly complex protein mixtures such as wound fluids using mass spectrometry. For this the used immunodepletion kit is a first but important step in order to represent the entire dynamic range of highly complex protein mixtures in the future.</p

The intestinal expulsion of the roundworm Ascaris suum is associated with eosinophils, intra-epithelial T cells and decreased intestinal transit time

Ascaris lumbricoides remains the most common endoparasite in humans, yet there is still very little information available about the immunological principles of protection, especially those directed against larval stages. Due to the natural host-parasite relationship, pigs infected with A. suum make an excellent model to study the mechanisms of protection against this nematode. In pigs, a self-cure reaction eliminates most larvae from the small intestine between 14 and 21 days post infection. In this study, we investigated the mucosal immune response leading to the expulsion of A. suum and the contribution of the hepato-tracheal migration. Self-cure was independent of previous passage through the liver or lungs, as infection with lung stage larvae did not impair self-cure. When animals were infected with 14-day-old intestinal larvae, the larvae were being driven distally in the small intestine around 7 days post infection but by 18 days post infection they re-inhabited the proximal part of the small intestine, indicating that more developed larvae can counter the expulsion mechanism. Self-cure was consistently associated with eosinophilia and intra-epithelial T cells in the jejunum. Furthermore, we identified increased gut movement as a possible mechanism of self-cure as the small intestinal transit time was markedly decreased at the time of expulsion of the worms. Taken together, these results shed new light on the mechanisms of self-cure that occur during A. suum infections

Temporal variance of disturbance did not affect diversity and structure of a marine fouling community in north-eastern New Zealand

Natural heterogeneity in ecological parameters, like population abundance, is more widely recognized and investigated than variability in the processes that control these parameters. Experimental ecologists have focused mainly on the mean intensity of predictor variables and have largely ignored the potential to manipulate variances in processes, which can be considered explicitly in experimental designs to explore variation in causal mechanisms. In the present study, the effect of the temporal variance of disturbance on the diversity of marine assemblages was tested in a field experiment replicated at two sites on the northeast coast of New Zealand. Fouling communities grown on artificial settlement substrata experienced disturbance regimes that differed in their inherent levels of temporal variability and timing of disturbance events, while disturbance intensity was identical across all levels. Additionally, undisturbed assemblages were used as controls. After 150 days of experimental duration, the assemblages were then compared with regard to their species richness, abundance and structure. The disturbance effectively reduced the average total cover of the assemblages, but no consistent effect of variability in the disturbance regime on the assemblages was detected. The results of this study were corroborated by the outcomes from simultaneous replicate experiments carried out in each of eight different biogeographical regions around the world