Intra- and inter-individual genetic differences in gene expression

Genetic variation is known to influence the amount of mRNA produced by a gene. Given that the molecular machines control mRNA levels of multiple genes, we expect genetic variation in the components of these machines would influence multiple genes in a similar fashion. In this study we show that this assumption is correct by using correlation of mRNA levels measured independently in the brain, kidney or liver of multiple, genetically typed, mice strains to detect shared genetic influences. These correlating groups of genes (CGG) have collective properties that account for 40-90% of the variability of their constituent genes and in some cases, but not all, contain genes encoding functionally related proteins. Critically, we show that the genetic influences are essentially tissue specific and consequently the same genetic variations in the one animal may up-regulate a CGG in one tissue but down-regulate the same CGG in a second tissue. We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals. The implication of this study is that this class of genetic variation can result in complex inter- and intra-individual and tissue differences and that this will create substantial challenges to the investigation of phenotypic outcomes, particularly in humans where multiple tissues are not readily available.

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Geometric Interpretation of Gene Coexpression Network Analysis

The merging of network theory and microarray data analysis techniques has spawned a new field: gene coexpression network analysis. While network methods are increasingly used in biology, the network vocabulary of computational biologists tends to be far more limited than that of, say, social network theorists. Here we review and propose several potentially useful network concepts. We take advantage of the relationship between network theory and the field of microarray data analysis to clarify the meaning of and the relationship among network concepts in gene coexpression networks. Network theory offers a wealth of intuitive concepts for describing the pairwise relationships among genes, which are depicted in cluster trees and heat maps. Conversely, microarray data analysis techniques (singular value decomposition, tests of differential expression) can also be used to address difficult problems in network theory. We describe conditions when a close relationship exists between network analysis and microarray data analysis techniques, and provide a rough dictionary for translating between the two fields. Using the angular interpretation of correlations, we provide a geometric interpretation of network theoretic concepts and derive unexpected relationships among them. We use the singular value decomposition of module expression data to characterize approximately factorizable gene coexpression networks, i.e., adjacency matrices that factor into node specific contributions. High and low level views of coexpression networks allow us to study the relationships among modules and among module genes, respectively. We characterize coexpression networks where hub genes are significant with respect to a microarray sample trait and show that the network concept of intramodular connectivity can be interpreted as a fuzzy measure of module membership. We illustrate our results using human, mouse, and yeast microarray gene expression data. The unification of coexpression network methods with traditional data mining methods can inform the application and development of systems biologic methods

Current anti-doping policy: a critical appraisal

BACKGROUND: Current anti-doping in competitive sports is advocated for reasons of fair-play and concern for the athlete's health. With the inception of the World Anti Doping Agency (WADA), anti-doping effort has been considerably intensified. Resources invested in anti-doping are rising steeply and increasingly involve public funding. Most of the effort concerns elite athletes with much less impact on amateur sports and the general public. DISCUSSION: We review this recent development of increasingly severe anti-doping control measures and find them based on questionable ethical grounds. The ethical foundation of the war on doping consists of largely unsubstantiated assumptions about fairness in sports and the concept of a "level playing field". Moreover, it relies on dubious claims about the protection of an athlete's health and the value of the essentialist view that sports achievements reflect natural capacities. In addition, costly antidoping efforts in elite competitive sports concern only a small fraction of the population. From a public health perspective this is problematic since the high prevalence of uncontrolled, medically unsupervised doping practiced in amateur sports and doping-like behaviour in the general population (substance use for performance enhancement outside sport) exposes greater numbers of people to potential harm. In addition, anti-doping has pushed doping and doping-like behaviour underground, thus fostering dangerous practices such as sharing needles for injection. Finally, we argue that the involvement of the medical profession in doping and anti-doping challenges the principles of non-maleficience and of privacy protection. As such, current anti-doping measures potentially introduce problems of greater impact than are solved, and place physicians working with athletes or in anti-doping settings in an ethically difficult position. In response, we argue on behalf of enhancement practices in sports within a framework of medical supervision. SUMMARY: Current anti-doping strategy is aimed at eradication of doping in elite sports by means of all-out repression, buttressed by a war-like ideology similar to the public discourse sustaining international efforts against illicit drugs. Rather than striving for eradication of doping in sports, which appears to be an unattainable goal, a more pragmatic approach aimed at controlled use and harm reduction may be a viable alternative to cope with doping and doping-like behaviour

Comparison of threshold selection methods for microarray gene co-expression matrices.

ABSTRACT: BACKGROUND: Network and clustering analyses of microarray co-expression correlation data often require application of a threshold to discard small correlations, thus reducing computational demands and decreasing the number of uninformative correlations. This study investigated threshold selection in the context of combinatorial network analysis of transcriptome data. FINDINGS: Six conceptually diverse methods - based on number of maximal cliques, correlation of control spots with expressed genes, top 1% of correlations, spectral graph clustering, Bonferroni correction of p-values, and statistical power - were used to estimate a correlation threshold for three time-series microarray datasets. The validity of thresholds was tested by comparison to thresholds derived from Gene Ontology information. Stability and reliability of the best methods were evaluated with block bootstrapping.Two threshold methods, number of maximal cliques and spectral graph, used information in the correlation matrix structure and performed well in terms of stability. Comparison to Gene Ontology found thresholds from number of maximal cliques extracted from a co-expression matrix were the most biologically valid. Approaches to improve both methods were suggested. CONCLUSION: Threshold selection approaches based on network structure of gene relationships gave thresholds with greater relevance to curated biological relationships than approaches based on statistical pair-wise relationships

Ruminal Protozoal Populations of Angus Steers Differing in Feed Efficiency

Feed accounts for as much as 70% of beef production costs, and improvement of the efficiency with which animals convert feed to product has the potential to have substantial financial impact on the beef industry. The rumen microbiome plays a key role in determining feed efficiency; however, previous studies of rumen microbiota have not focused on protozoal communities despite the estimation that these organisms represent approximately 50% of rumen content biomass. Protozoal communities participate in the regulation of bacterial populations and nitrogen cycling—key aspects of microbiome dynamics. The present study focused on identifying potential associations of protozoal community profiles with feed efficiency. Weaned steers (n = 50) 7 months of age weighing approximately 260 kg were adapted to a growing ration and GrowSafe for 2 weeks prior to a 70-day feed efficiency trial. The GrowSafe system is a feeding system that monitors feed intake in real time. Body weights were collected on the first day and then every 7 days of the feed efficiency trial, and on the final day, approximately 50 mL of rumen content were collected via orogastric tubing and frozen at −80 ◦C. Body weight and feed intake were used to calculate residual feed intake (RFI) as a measure of feed efficiency, and steers were categorized as high (n = 14) or low (n = 10) RFI based on ±0.5 standard deviations about the mean RFI. Microbial DNA was extracted, and the eukaryotic component profiled by amplification and sequencing of 18S genes using degenerate primers that can amplify this locus across a range of protists. The taxonomy of protozoal sequences was assigned using QIIME 1.9 and analyzed using QIIME and SAS 9.4 with significance determined at α ≤ 0.05. Greater abundances of unassigned taxa were associated with high-RFI steers (p = 0.03), indicating a need for further study to identify component protozoal species. Differences were observed between low- and high-RFI steers in protozoal community phylogenetic diversity, including weighted beta-diversity (p = 0.04), Faith’s phylogenetic diversity (p = 0.03), and observed Operational taxonomic unit (OTU) (p = 0.03). The unassigned taxa and differences in phylogenetic diversity of protozoal communities may contribute to divergences observed in feed efficiency phenotypes in beef steers

Ginsenoside Rc from Panax Ginseng Ameliorates Palmitate-Induced UB/OC-2 Cochlear Cell Injury

By 2050, at least 700 million people will require hearing therapy while 2.5 billion are projected to suffer from hearing loss. Sensorineural hearing loss (SNHL) arises from the inability of the inner ear to convert fluid waves into neural electric signals because of injury to cochlear hair cells that has resulted in their death. In addition, systemic chronic inflammation implicated in other pathologies may exacerbate cell death leading to SNHL. Phytochemicals have emerged as a possible solution because of the growing evidence of their anti-inflammatory, antioxidant, and anti-apoptotic properties. Ginseng and its bioactive molecules, ginsenosides, exhibit effects that suppress pro-inflammatory signaling and protect against apoptosis. In the current study, we investigated the effects of ginsenoside Rc (G-Rc) on UB/OC-2 primary murine sensory hair cell survival in response to palmitate-induced injury. G-Rc promoted UB/OC-2 cell survival and cell cycle progression. Additionally, G-Rc enhanced the differentiation of UB/OC-2 cells into functional sensory hair cells and alleviated palmitate-induced inflammation, endoplasmic reticulum stress, and apoptosis. The current study offers novel insights into the effects of G-Rc as a potential adjuvant for SNHL and warrants further studies elucidating the molecular mechanisms

Molecular basis for hair loss in mice carrying a novel nonsense mutation (Hrrh-R ) in the hairless gene (Hr).

Animal models carrying mutations in the hairless (Hr) gene provide a rich resource for study of hair follicle biology. A spontaneous mouse mutant with a phenotype strikingly similar to rhino mutants of Hr arose spontaneously in the mouse facility at Oak Ridge National Laboratory. Sequence analysis of Hr in these mutants uncovered a nonsense mutation in exon 12, designated as Hr(rh-R) (rhino, Oak Ridge). The mutation led to significant reduction in Hr mRNA levels, predicted to be due to nonsense-mediated decay. Histological analysis indicated dilated hair follicle infundibula at 14 days of age that rapidly became filled with cornified material. Microarray analyses revealed that expression levels of many genes involved in keratinocyte differentiation, epidermal regeneration, and wound healing were significantly upregulated before morphological detection of the phenotype, suggesting their role in onset of the Hr(rh-R) phenotype. Identification of this new Hr allele and the underlying molecular alterations allows further understanding of the role of Hr in hair follicle biology