Protein expression changes in skeletal muscle in response to growth promoter abuse in beef cattle

The fraudulent treatment of cattle with growth promoting agents (GPAs) is a matter of great concern for the European Union (EU) authorities and consumers. It has been estimated that 10% of animals are being illegally treated in the EU. In contrast, only a much lower percentage of animals (<0.5%) are actually found as being noncompliant by conventional analytical methods. Thus, it has been proposed that methods should be developed that can detect the use of the substances via the biological effects of these substances on target organs, such as the alteration of protein expression profiles. Here we present a study aimed at evaluating if a correlation exists between the treatment with GPAs and alterations in the two-dimensional electrophoresis (2DE) protein pattern obtained from the biceps brachii skeletal muscle from mixed-bred cattle. After image analysis and statistical evaluation, protein spots that differentiate between treated and control groups were selected for analysis by mass spectrometry. A set of proteins could be defined that accurately detect the use of glucocorticoids and β(2)-agonists as growth promoters through the changes caused in muscle differentiation. As a further validation, we repeated the analysis using an independent set of samples from a strain of pure-bred cattle and verified these proteins by Western blot analysis.     

Simple screening method for differentially methylated regions of the genome using a small number of cells

Genomic DNA methylation is a major epigenetic mechanism controlling the expression of genetic information. Therefore, identifying regions of the genome that are differentially methylated in different cells is a useful strategy for the study of biological phenomena. To date, several useful screening methods have been established for identifying differentially methylated genomic regions. However, it is impossible to use these methods in fields of study in which it is difficult to obtain a large number of uniform cells, because considerable amounts of genomic DNA are required. Given this situation, we developed a method for preparing large genomic DNA from a small number of cells, and a simple and highly sensitive method for screening for differentially methylated sites. Combined, these two methods comprise a simple screening method, which we named "Differential Methylation Site Scanning" (DMSS), for identifying differentially methylated regions of the genome from a small number of cells. Just 10 cells are sufficient for the method described here.     

Thymus and meat physicochemical measurements to discriminate calves treated with anabolic and therapeutic doses of dexamethasone

To preserve the Europe consumers' health, the use of glucocorticoids as growth promoters is prohibited in cattle fattening. In 2008, the Italian Ministry of Health associated to the official control a national monitoring plan based on the histological thymus analysis to identify animals illegally treated with corticosteroids. However, since corticosteroids are authorized and widely used for therapeutic purposes, it is necessary to verify whether the thymus histological test and some physicochemical traits in meat are able to discriminate doped calves from dexamethasone therapeutic treated ones. The aims of this study were (i) to establish whether the therapeutic and illicit corticosteroid treatments of calves could be differentiated through histological evaluation of thymus and by physicochemical meat traits; (ii) to identify a restricted number of physicochemical traits that could differentiate dexamethasone treated from untreated calves. Three groups of 15 calves each were included in this study: group dexamethasone therapeutic treatment treated with dexamethasone 21-phosphate disodium salt at a therapeutic dose (2 mg/kg of live weight for three consecutive days); group dexamethasone anabolic treatment orally treated with dexamethasone 21-phosphate disodium salt according to a presumed anabolic protocol (0.4 mg/day per animal for 20 days); group placebo control treated with a placebo served as control. Results demonstrated that groups could be easily discriminated by thymus microscopy as well as by two meat markers, namely, cooking loss and shear firmness or Warner-Bratzler shear force. The combination of thymus microscopic features and meat physicochemical traits could be used as a practical, economic and accurate screening strategy to discriminate between meat from illegally and therapeutically treated calves. This new reliable and simple tool could contribute to identify animals treated with dexamethasone in those countries where glucocorticoids are illegally used as growth promoters. More in general, this system could be included in the framework of official controls, and applied to verify suppliers' reliability by the meat industry.     

The Energetic Metabolism of the European Union and the United States: Decadal Energy Input Time-Series with an Emphasis on Biomass

This article presents an assessment of energy inputs of the European Union (the 15 countries before the 2004 enlargement, abbreviated EU‐15) for the period 1970–2001 and the United States for 1980–2000. The data are based on an energy flow analysis (EFA) that evaluates socioeconomic energy flows in a way that is conceptually consistent with current materials flow analysis (MFA) methods. EFA allows assessment of the total amount of energy required by a national economy; it yields measures of the size of economic systems in biophysical units. In contrast to conventional energy balances, which only include technically used energy, EFA also accounts for socioeconomic inputs of biomass; that is, it also considers food, feed, wood and other materials of biological origin. The energy flow accounts presented in this article do not include embodied energy. Energy flow analyses are relevant for comparisons across modes of subsistence (e.g., agrarian and industrial society) and also to detect interrelations between energy utilization and land use. In the EU‐15, domestic energy consumption (DEC = apparent consumption = domestic extraction plus import minus export) grew from 60 exajoules per year (1 EJ = 10¹⁸ J) in 1970 to 79 EJ/yr in 2001, thus exceeding its territory's net primary production (NPP, a measure of the energy throughput of ecosystems). In the United States, DEC increased from 102 EJ/yr in 1980 to 125 EJ/yr in 2000 and was thus slightly smaller than its NPP. Taken together, the EU‐15 and the United States accounted for about 38% of global technical energy use, 31% of humanity's energetic metabolism, but only 10% of global terrestrial NPP and 11% of world population in the early 1990s. Per capita DEC of the United States is more than twice that of the EU‐15. Calculated according to EFA methods, energy input in the EU and the United States was between one‐fifth and one‐third above the corresponding value reported in conventional energy balances. The article discusses implications of these results for sustainability, as well as future research needs.     

Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology.     

Uncloaking a Lost Cause: Decolonizing ancestry estimation in the United States

Since the professionalization of US-based forensic anthropology in the 1970s, ancestry estimation has been included as a standard part of the biological profile, because practitioners have assumed it necessary to achieve identifications in medicolegal contexts. Simultaneously, forensic anthropologists have not fully considered the racist context of the criminal justice system in the United States related to the treatment of Black, Indigenous, and People of Color; nor have we considered that ancestry estimation might actually hinder identification efforts because of entrenched racial biases. Despite ongoing criticisms from mainstream biological anthropology that ancestry estimation perpetuates race science, forensic anthropologists have continued the practice. Recent years have seen the prolific development of retooled typological approaches with 21st century statistical prowess to include methods for estimating ancestry from cranial morphoscopic traits, despite no evidence that these traits reflect microevolutionary processes or are suitable genetic proxies for population structure; and such approaches have failed to critically evaluate the societal consequences for perpetuating the biological race concept. Around the country, these methods are enculturated in every aspect of the discipline ranging from university classrooms, to the board-certification examination marking the culmination of training, to standard operating procedures adopted by forensic anthropology laboratories. Here, we use critical race theory to interrogate the approaches utilized to estimate ancestry to include a critique of the continued use of morphoscopic traits, and we assert that the practice of ancestry estimation contributes to white supremacy. Based on the lack of scientific support that these traits reflect evolutionary history, and the inability to disentangle skeletal-based ancestry estimates from supporting the biological validity of race, we urge all forensic anthropologists to abolish the practice of ancestry estimation.     

Binary analysis and optimization-based normalization of gene expression data

MOTIVATION: Most approaches to gene expression analysis use real-valued expression data, produced by high-throughput screening technologies, such as microarrays. Often, some measure of similarity must be computed in order to extract meaningful information from the observed data. The choice of this similarity measure frequently has a profound effect on the results of the analysis, yet no standards exist to guide the researcher. RESULTS: To address this issue, we propose to analyse gene expression data entirely in the binary domain. The natural measure of similarity becomes the Hamming distance and reflects the notion of similarity used by biologists. We also develop a novel data-dependent optimization-based method, based on Genetic Algorithms (GAs), for normalizing gene expression data. This is a necessary step before quantizing gene expression data into the binary domain and generally, for comparing data between different arrays. We then present an algorithm for binarizing gene expression data and illustrate the use of the above methods on two different sets of data. Using Multidimensional Scaling, we show that a reasonable degree of separation between different tumor types in each data set can be achieved by working solely in the binary domain. The binary approach offers several advantages, such as noise resilience and computational efficiency, making it a viable approach to extracting meaningful biological information from gene expression data.     

Comparison of metabolite production capability indices generated by network analysis methods

A framework of constraint-based reconstruction and analysis (COBRA) is used for modeling large-scale metabolic networks. In COBRA, extreme pathway and optimization analyses are commonly used to study the properties of networks. While the results of both methods are completely consistent, extreme pathway analysis is considered to be better because of its wider representational ability. In this study, we assessed these two methods by computational knockout experiments. We examined a simple pathway model and found that the extreme pathway method led to misguided conclusions in specific cases, while optimization analysis calculated the correct knockout effects. We also investigated the Escherichia coli metabolic pathway model, and found that these methods result in inconsistent interpretations of the network properties. IN CONCLUSION: it has been claimed that these two methods result in the same producible metabolites, but we found a difference in individual results for a biological pathway. Our results could provide helpful guidance for when to use the methods, particularly extreme pathway analysis.     

High-capacity screening of arylalkylamine N-acetyltransferase inhibitors using a high-performance liquid chromatography system

Systematic screening is a natural development of any pharmacological program. Most enzyme inhibitor screens use indirect or "aspecific" methods, such as colorimetric or fluorimetric ones. These screening methods cause quite a few false-positive and false-negative hits. In order to limit these as much as possible, we developed a methodology using a high-performance liquid chromatography (HPLC) system for the medium throughput screening of serotonin N-acetyltransferase inhibitors. The core of this screening system is (1) the dramatic shortening of the analytical time down to 100 s per run by using a high-performance analytical column (Turbo), and (2) the use of absorption as opposed to radioactivity for detection of the product of the reaction (N-acetylserotonin). This system permits the analysis of about 1,000 compounds per day to be performed with a single HPLC system. This enzymatic system was taken as an example, because the methodology can be extended to many other enzymes, particularly transferases, phosphatases, and kinases.     

LC-HRMS based metabolomics screening model to detect various β-agonists treatments in bovines

A study was performed to determine if untargeted metabolomics of urine could be used to establish a predictive tool for identifying β-agonists misuse in cattle. Although prohibited for more than 20 years within the EU, growth promoting practices for livestock fattening purposes are still suspected. Methods based on gas- or liquid chromatography coupled to (tandem) mass spectrometry are today considered as the state-of-the-art to monitor, in a targeted approach, residues of known drugs. To overcome the detection of anabolic practices, new synthetic xenobiotic growth promoters have been designed and new ways of applications, such as the administration of low dose cocktails, have been developed. In this context, innovative screening strategies are urgently needed to enable efficient control of such practices. Omic technologies have recently shown their relevance in highlighting physiological response resulting from anabolic compounds administration. LC-HRMS based metabolomics is one of the approaches allowing profiling biological matrices to reveal biological effects of a drug. In the present work, a metabolomics study performed on urine samples collected in the frame of several independent experiments involving different animals, different β-agonists treatments and different parameters (doses, compounds mixture, treatment length), allowed highlighting biomarkers of interest and implementing a robust statistical model to predict for β-agonists treated bovines. Performances of the proposed model fit with EU requirements for screening methods.     

Protein complexes are central in the yeast genetic landscape

If perturbing two genes together has a stronger or weaker effect than expected, they are said to genetically interact. Genetic interactions are important because they help map gene function, and functionally related genes have similar genetic interaction patterns. Mapping quantitative (positive and negative) genetic interactions on a global scale has recently become possible. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, like biological processes or complexes, or connections between modules. However it is not yet known how these patterns globally relate to known functional modules. Here we systematically study the monochromatic nature of known biological processes using the largest quantitative genetic interaction data set available, which includes fitness measurements for ～5.4 million gene pairs in the yeast Saccharomyces cerevisiae. We find that only 10% of biological processes, as defined by Gene Ontology annotations, and less than 1% of inter-process connections are monochromatic. Further, we show that protein complexes are responsible for a surprisingly large fraction of these patterns. This suggests that complexes play a central role in shaping the monochromatic landscape of biological processes. Altogether this work shows that both positive and negative monochromatic patterns are found in known biological processes and in their connections and that protein complexes play an important role in these patterns. The monochromatic processes, complexes and connections we find chart a hierarchical and modular map of sensitive and redundant biological systems in the yeast cell that will be useful for gene function prediction and comparison across phenotypes and organisms. Furthermore the analysis methods we develop are applicable to other species for which genetic interactions will progressively become more available.     

A picture is worth a thousand words: Genomics to phenomics in the yeast Saccharomyces cerevisiae

Large scale cell biological experiments are beginning to be applied as a systems-level approach to decipher mechanisms that govern cellular function in health and disease. The use of automated microscopes combined with digital imaging, machine learning and other analytical tools has enabled high-content screening (HCS) in a variety of experimental systems. Successful HCS screens demand careful attention to assay development, data acquisition methods and available genomic tools. In this minireview, we highlight developments in this field pertaining to yeast cell biology and discuss how we have combined HCS with methods for automated yeast genetics (synthetic genetic array (SGA) analysis) to enable systematic analysis of cell biological phenotypes in a variety of genetic backgrounds.     

Improved power by use of a weighted score test for linkage disequilibrium mapping

Association studies offer an exciting approach to finding underlying genetic variants of complex human diseases. However, identification of genetic variants still includes difficult challenges, and it is important to develop powerful new statistical methods. Currently, association methods may depend on single-locus analysis--that is, analysis of the association of one locus, which is typically a single-nucleotide polymorphism (SNP), at a time--or on multilocus analysis, in which multiple SNPs are used to allow extraction of maximum information about linkage disequilibrium (LD). It has been shown that single-locus analysis may have low power because a single SNP often has limited LD information. Multilocus analysis, which is more informative, can be performed on the basis of either haplotypes or genotypes. It may lose power because of the often large number of degrees of freedom involved. The ideal method must make full use of important information from multiple loci but avoid increasing the degrees of freedom. Therefore, we propose a method to capture information from multiple SNPs but with the use of fewer degrees of freedom. When a set of SNPs in a block are correlated because of LD, we might expect that the genotype variation among the different phenotypic groups would extend across all the SNPs, and this information could be compressed into the low-frequency components of a Fourier transform. Therefore, we develop a test based on weighted Fourier transformation coefficients, with more weight given to the low-frequency components. Our simulation results demonstrate the validity and substantially higher power of the proposed method compared with other common methods. This method provides an additional tool to existing methods for identification of causative genetic variants underlying complex diseases.     

An improved method for the determination of green and black tea polyphenols in biomatrices by high-performance liquid chromatography with coulometric array detection

Tea polyphenols are strong antioxidants and are believed to have beneficial health effects. However, the blood and tissue levels of these compounds are not well characterized because of a lack of suitable analytical methods for the biological resolution of these compounds. Previously, we developed methods for the analysis of three green tea catechins. Now we report an improved method for the measurement of the levels of the different catechins and theaflavins in biological fluids and tissues. The method includes digestion of the plasma, urine, or tissue samples with beta-d-glucuronidase and sulfatase, followed by extraction with ethyl acetate and subsequent separation by reversed-phase high-performance liquid chromatography (HPLC). The polyphenols are identified on the basis of their retention times, spectral analysis, and electrochemical behavior across an array of electrodes. In a single HPLC run, it is possible to determine the major catechins and theaflavins as well as some of the catechin metabolites. The detection limits for catechins and theaflavins are from 5 to 10 ng/ml of saliva, plasma, or urine.     

Purification and characterization of natural Ara h 8, the Bet v 1 homologous allergen from peanut, provides a novel isoform

The peanut allergen Ara h 8 is an important allergen for birch pollen allergic patients because of the cross-reactivity to the homologous Bet v 1. As the existence of Ara h 8 has been shown at the cDNA level so far (AY328088) and the allergen has indirectly been detected as natural protein, it was the aim of our study to identify natural Ara h 8 in peanut extract and to develop a purification strategy. This was achieved using a unique combination of purification steps, including optimized extraction conditions, size exclusion and ion exchange chromatography and treatment of the interfering contaminants with iodoacetic acid. A characterization of the protein by microsequencing showed discrepancies to the deduced amino acid sequence of AY328088. For this reason, we cloned and expressed a new Ara h 8 isoform from cDNA (EU046325). This IgE-reactive protein corresponds to the results of microsequencing, ESI-FTICR-MS and trypsin fingerprinting analysis of the authentic and purified nAra h 8. Apart from the ultimate use of recombinant allergens for diagnostic procedures, there is also a scientific need for the natural counterpart, as it represents an excellent reference point by which to compare protein characteristics and to standardize diagnostic and therapeutic allergens.     

Combined Force-Torque Spectroscopy of Proteins by Means of Multiscale Molecular Simulation

Assessing the structural properties of large proteins is important to gain an understanding of their function in, e.g., biological systems or biomedical applications. We propose a method to examine the mechanical properties of proteins subject to applied forces by means of multiscale simulation. Both stretching and torsional forces are considered, and these may be applied independently of each other. As a proof of principle, we apply torsional forces to a coarse-grained continuum model of the antibody protein immunoglobulin G using fluctuating finite element analysis and use it to identify the area of strongest deformation. This region is essential to the torsional properties of the molecule as a whole because it represents the softest, most deformable domain. Zooming in, this part of the molecule is subjected to torques and stretching forces using molecular dynamics simulations on an atomistically resolved level to investigate its torsional properties. We calculate the torsional resistance as a function of the rotation of the domain while subjecting it to various stretching forces. From this, we assess how the measured twist-torque profiles develop with increasing stretching force and show that they exhibit torsion stiffening, in qualitative agreement with experimental findings. We argue that combining the twist-torque profiles for various stretching forces effectively results in a combined force-torque spectroscopy analysis, which may serve as a mechanical signature for a biological macromolecule.     

High-throughput identification of phage-derived imaging agents

The use of phage-displayed peptide libraries is a powerful method for selecting peptides with desired binding properties. However, the validation and prioritization of "hits" obtained from this screening approach remains challenging. Here, we describe the development and testing of a new analysis method to identify and display hits from phage-display experiments and high-throughput enzyme-linked immunosorbent assay screens. We test the method using a phage screen against activated macrophages to develop imaging agents with higher specificity for active disease processes. The new methodology should be useful in identifying phage hits and is extendable to other library screening methods such as small-molecule and nanoparticle libraries.     

Detection and determination of antimalarial drugs and their metabolites in body fluids

This review of methods for determining antimalarial drugs in biological fluids has focused on the various analytical techniques for the assay of chloroquine, quinine, amodiaquine, mefloquine, proguanil, pyrimethamine, sulphadoxine, primaquine and some of their metabolites. The methods for determining antimalarials and their metabolites in biological samples have changed rapidly during the last eight to ten years with the increased use of chromatographic techniques. Chloroquine is still the most used antimalarial drug, and various methods of different complexity exist for the determination of chloroquine and its metabolites in biological fluids. The pharmacokinetics of chloroquine and other antimalarials have been updated using these new methods.The various analytical techniques have been discussed, from simple colorimetric methods of intermediate selectivity and sensitivity to highly sophisticated, selective and sensitive chromatographic methods applied in a modern analytical laboratory. Knowledge concerning the method for a particular study is determined by the type of application and the facilities, equipment and personnel available. Often is it useful to apply various methods when conducting a clinical study in malaria-endemic areas. Field-adapted methods for the analysis of urine samples can be applied at the study site for screening, and corresponding blood samples can be preserved for subsequent analysis in the laboratory. Selecting samples for laboratory analysis is based on clinical, parasitological and field-assay data. The wide array of methods available for chloroquine permit carefully tailored approaches to acquire the necessary analytical information in clinical field studied concerning the use of this drug. The development of additional field-adapted and field-interfaced methods for other commonly used antimalarials will provide similar flexibility in field studies of these drugs.