A False Start in the Race Against Doping in Sport: Concerns With Cycling’s Biological Passport

Professional cycling has suffered from a number of doping scandals. The sport’s governing bodies have responded by implementing an aggressive new antidoping program known as the biological passport. Cycling’s biological passport marks a departure from traditional antidoping efforts, which have focused on directly detecting prohibited substances in a cyclist’s system. Instead, the biological passport tracks biological variables in a cyclist’s blood and urine over time, monitoring for fluctuations that are thought to indirectly reveal the effects of doping. Although this method of indirect detection is promising, it also raises serious legal and scientific concerns. Since its introduction, the cycling community has debated the reliability of indirect biological-passport evidence and the clarity, consistency, and transparency of its use in proving doping violations. Such uncertainty undermines the legitimacy of finding cyclists guilty of doping based on this indirect evidence alone. Antidoping authorities should address these important concerns before continuing to pursue doping sanctions against cyclists solely on the basis of their biological passports

CMASA: an accurate algorithm for detecting local protein structural similarity and its application to enzyme catalytic site annotation

<p>Abstract</p> <p>Background</p> <p>The rapid development of structural genomics has resulted in many "unknown function" proteins being deposited in Protein Data Bank (PDB), thus, the functional prediction of these proteins has become a challenge for structural bioinformatics. Several sequence-based and structure-based methods have been developed to predict protein function, but these methods need to be improved further, such as, enhancing the accuracy, sensitivity, and the computational speed. Here, an accurate algorithm, the CMASA (Contact MAtrix based local Structural Alignment algorithm), has been developed to predict unknown functions of proteins based on the local protein structural similarity. This algorithm has been evaluated by building a test set including 164 enzyme families, and also been compared to other methods.</p> <p>Results</p> <p>The evaluation of CMASA shows that the CMASA is highly accurate (0.96), sensitive (0.86), and fast enough to be used in the large-scale functional annotation. Comparing to both sequence-based and global structure-based methods, not only the CMASA can find remote homologous proteins, but also can find the active site convergence. Comparing to other local structure comparison-based methods, the CMASA can obtain the better performance than both FFF (a method using geometry to predict protein function) and SPASM (a local structure alignment method); and the CMASA is more sensitive than PINTS and is more accurate than JESS (both are local structure alignment methods). The CMASA was applied to annotate the enzyme catalytic sites of the non-redundant PDB, and at least 166 putative catalytic sites have been suggested, these sites can not be observed by the Catalytic Site Atlas (CSA).</p> <p>Conclusions</p> <p>The CMASA is an accurate algorithm for detecting local protein structural similarity, and it holds several advantages in predicting enzyme active sites. The CMASA can be used in large-scale enzyme active site annotation. The CMASA can be available by the mail-based server (<url>http://159.226.149.45/other1/CMASA/CMASA.htm</url>).</p

Establishment of a Novel Fluorescence-Based Method to Evaluate Chaperone-Mediated Autophagy in a Single Neuron

Background: Chaperone-mediated autophagy (CMA) is a selective autophagy-lysosome protein degradation pathway. The role of CMA in normal neuronal functions and in neural disease pathogenesis remains unclear, in part because there is no available method to monitor CMA activity at the single-cell level. Methodology/Principal Findings: We sought to establish a single-cell monitoring method by visualizing translocation of CMA substrates from the cytosol to lysosomes using the HaloTag (HT) system. GAPDH, a CMA substrate, was fused to HT (GAPDH-HT); this protein accumulated in the lysosomes of HeLa cells and cultured cerebellar Purkinje cells (PCs) after labeling with fluorescent dye-conjugated HT ligand. Lysosomal accumulation was enhanced by treatments that activate CMA and prevented by siRNA-mediated knockdown of LAMP2A, a lysosomal receptor for CMA, and by treatments that inactivate CMA. These results suggest that lysosomal accumulation of GAPDH-HT reflects CMA activity. Using this method, we revealed that mutant cPKC, which causes spinocerebellar ataxia type 14, decreased CMA activity in cultured PCs. Conclusion/Significance: In the present study, we established a novel fluorescent-based method to evaluate CMA activity in a single neuron. This novel method should be useful and valuable for evaluating the role of CMA in various neurona

Discovering cancer genes by integrating network and functional properties

<p>Abstract</p> <p>Background</p> <p>Identification of novel cancer-causing genes is one of the main goals in cancer research. The rapid accumulation of genome-wide protein-protein interaction (PPI) data in humans has provided a new basis for studying the topological features of cancer genes in cellular networks. It is important to integrate multiple genomic data sources, including PPI networks, protein domains and Gene Ontology (GO) annotations, to facilitate the identification of cancer genes.</p> <p>Methods</p> <p>Topological features of the PPI network, as well as protein domain compositions, enrichment of gene ontology categories, sequence and evolutionary conservation features were extracted and compared between cancer genes and other genes. The predictive power of various classifiers for identification of cancer genes was evaluated by cross validation. Experimental validation of a subset of the prediction results was conducted using siRNA knockdown and viability assays in human colon cancer cell line DLD-1.</p> <p>Results</p> <p>Cross validation demonstrated advantageous performance of classifiers based on support vector machines (SVMs) with the inclusion of the topological features from the PPI network, protein domain compositions and GO annotations. We then applied the trained SVM classifier to human genes to prioritize putative cancer genes. siRNA knock-down of several SVM predicted cancer genes displayed greatly reduced cell viability in human colon cancer cell line DLD-1.</p> <p>Conclusion</p> <p>Topological features of PPI networks, protein domain compositions and GO annotations are good predictors of cancer genes. The SVM classifier integrates multiple features and as such is useful for prioritizing candidate cancer genes for experimental validations.</p

Methodological Deficits in Diagnostic Research Using ‘-Omics’ Technologies: Evaluation of the QUADOMICS Tool and Quality of Recently Published Studies

Background: QUADOMICS is an adaptation of QUADAS (a quality assessment tool for use in systematic reviews of diagnostic accuracy studies), which takes into account the particular challenges presented by '-omics' based technologies. Our primary objective was to evaluate the applicability and consistency of QUADOMICS. Subsequently we evaluated and describe the methodological quality of a sample of recently published studies using the tool. Methodology/Principal Findings: 45'-omics'- based diagnostic studies were identified by systematic search of Pubmed using suitable MeSH terms (>Genomics>, >Sensitivity and specificity>, >Diagnosis>). Three investigators independently assessed the quality of the articles using QUADOMICS and met to compare observations and generate a consensus. Consistency and applicability was assessed by comparing each reviewer's original rating with the consensus. Methodological quality was described using the consensus rating. Agreement was above 80% for all three reviewers. Four items presented difficulties with application, mostly due to the lack of a clearly defined gold standard. Methodological quality of our sample was poor; studies met roughly half of the applied criteria (mean ± sd, 54.7±18.4°%). Few studies were carried out in a population that mirrored the clinical situation in which the test would be used in practice, (6, 13.3%);none described patient recruitment sufficiently; and less than half described clinical and physiological factors that might influence the biomarker profile (20, 44.4%). Conclusions: The QUADOMICS tool can consistently be applied to diagnostic '-omics' studies presently published in biomedical journals. A substantial proportion of reports in this research field fail to address design issues that are fundamental to make inferences relevant for patient care. © 2010 Parker et al.This work was supported by the Spanish Agency for Health Technology Assessment, Exp PI06/90311, Instituto de Salud Carlos III and CIBER en Epidemiología y Salud Pública (CIBERESP) in SpainPeer Reviewe

Analysis of BAC end sequences in oak, a keystone forest tree species, providing insight into the composition of its genome

<p>Abstract</p> <p>Background</p> <p>One of the key goals of oak genomics research is to identify genes of adaptive significance. This information may help to improve the conservation of adaptive genetic variation and the management of forests to increase their health and productivity. Deep-coverage large-insert genomic libraries are a crucial tool for attaining this objective. We report herein the construction of a BAC library for <it>Quercus robur</it>, its characterization and an analysis of BAC end sequences.</p> <p>Results</p> <p>The <it>Eco</it>RI library generated consisted of 92,160 clones, 7% of which had no insert. Levels of chloroplast and mitochondrial contamination were below 3% and 1%, respectively. Mean clone insert size was estimated at 135 kb. The library represents 12 haploid genome equivalents and, the likelihood of finding a particular oak sequence of interest is greater than 99%. Genome coverage was confirmed by PCR screening of the library with 60 unique genetic loci sampled from the genetic linkage map. In total, about 20,000 high-quality BAC end sequences (BESs) were generated by sequencing 15,000 clones. Roughly 5.88% of the combined BAC end sequence length corresponded to known retroelements while <it>ab initio </it>repeat detection methods identified 41 additional repeats. Collectively, characterized and novel repeats account for roughly 8.94% of the genome. Further analysis of the BESs revealed 1,823 putative genes suggesting at least 29,340 genes in the oak genome. BESs were aligned with the genome sequences of <it>Arabidopsis thaliana</it>, <it>Vitis vinifera </it>and <it>Populus trichocarpa</it>. One putative collinear microsyntenic region encoding an alcohol acyl transferase protein was observed between oak and chromosome 2 of <it>V. vinifera.</it></p> <p>Conclusions</p> <p>This BAC library provides a new resource for genomic studies, including SSR marker development, physical mapping, comparative genomics and genome sequencing. BES analysis provided insight into the structure of the oak genome. These sequences will be used in the assembly of a future genome sequence for oak.</p

Dentifrices, mouthwashes, and remineralization/caries arrestment strategies

While our knowledge of the dental caries process and its prevention has greatly advanced over the past fifty years, it is fair to state that the management of this disease at the level of the individual patient remains largely empirical. Recommendations for fluoride use by patients at different levels of caries risk are mainly based on the adage that more is better. There is a general understanding that the fluoride compound, concentration, frequency of use, duration of exposure, and method of delivery can influence fluoride efficacy. Two important factors are (1) the initial interaction of relatively high concentrations of fluoride with the tooth surface and plaque during application and (2) the retention of fluoride in oral fluids after application

The LabelHash algorithm for substructure matching

Background: There is an increasing number of proteins with known structure but unknown function. Determining their function would have a significant impact on understanding diseases and designing new therapeutics. However, experimental protein function determination is expensive and very time-consuming. Computational methods can facilitate function determination by identifying proteins that have high structural and chemical similarity. Results: We present LabelHash, a novel algorithm for matching substructural motifs to large collections of protein structures. The algorithm consists of two phases. In the first phase the proteins are preprocessed in a fashion that allows for instant lookup of partial matches to any motif. In the second phase, partial matches for a given motif are expanded to complete matches. The general applicability of the algorithm is demonstrated with three different case studies. First, we show that we can accurately identify members of the enolase superfamily with a single motif. Next, we demonstrate how LabelHash can complement SOIPPA, an algorithm for motif identification and pairwise substructure alignment. Finally, a large collection of Catalytic Site Atlas motifs is used to benchmark the performance of the algorithm. LabelHash runs very efficiently in parallel; matching a motif against all proteins in the 95 % sequence identity filtered non-redundant Protein Data Bank typically takes no more than a few minutes. The LabelHash algorithm is available through a web server and as a suite of standalone programs a

Identification of the Schistosoma mansoni TNF-Alpha Receptor Gene and the Effect of Human TNF-Alpha on the Parasite Gene Expression Profile

Schistosoma mansoni is the major causative agent of schistosomiasis in the Americas. This parasite takes advantage of host signaling molecules such as cytokines and hormones to complete its development inside the host. Tumor necrosis factor-alpha (TNF-α) is one of the most important host cytokines involved in the inflammatory response. When cercariae, the infective stage, penetrates the human skin the release of TNF-α is started. In this work the authors describe the complete sequence of a possible TNF-α receptor in S. mansoni and detect that the receptor is most highly expressed in cercariae among all life cycle stages. Aiming to mimic the situation at the site of skin penetration, cercariae were mechanically transformed in vitro into schistosomula and exposed to human TNF-α. Exposure of early-developing schistosomula to the human hormone caused a large-scale change in the expression of parasite genes. Exposure of adult worms to human TNF-α caused gene expression changes as well, and the set of parasite altered genes in the adult parasite was different from that of schistosomula. This work increases the number of known signaling pathways of the parasite, and opens new perspectives into understanding the molecular components of TNF-α response as well as into possibly interfering with parasite–host interaction

Whole genome sequence and manual annotation of Clostridium autoethanogenum, an industrially relevant bacterium

Clostridium autoethanogenum is an acetogenic bacterium capable of producing high value commodity chemicals and biofuels from the C1 gases present in synthesis gas. This common industrial waste gas can act as the sole energy and carbon source for the bacterium that converts the low value gaseous components into cellular building blocks and industrially relevant products via the action of the reductive acetyl-CoA (Wood-Ljungdahl) pathway. Current research efforts are focused on the enhancement and extension of product formation in this organism via synthetic biology approaches. However, crucial to metabolic modelling and directed pathway engineering is a reliable and comprehensively annotated genome sequence