Phytoestrogens

Collectively, plants contain several different families of natural products among which are compounds with weak estrogenic or antiestrogenic activity toward mammals. These compounds, termed phytoestrogens, include certain isoflavonoids, flavonoids, stilbenes, and lignans. The best-studied dietary phytoestrogens are the soy isoflavones and the flaxseed lignans. Their perceived health beneficial properties extend beyond hormone-dependent breast and prostate cancers and osteoporosis to include cognitive function, cardiovascular disease, immunity and inflammation, and reproduction and fertility. In the future, metabolic engineering of plants could generate novel and exquisitely controlled dietary sources with which to better assess the potential health beneficial effects of phytoestrogens

A genome-wide association study identifies protein quantitative trait loci (pQTLs)

There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts - cis effects, and elsewhere in the genome - trans effects. The role of genetic variation in determining protein levels has not been systematically assessed. Using a genome-wide association approach we show that common genetic variation influences levels of clinically relevant proteins in human serum and plasma. We evaluated the role of 496,032 polymorphisms on levels of 42 proteins measured in 1200 fasting individuals from the population based InCHIANTI study. Proteins included insulin, several interleukins, adipokines, chemokines, and liver function markers that are implicated in many common diseases including metabolic, inflammatory, and infectious conditions. We identified eight Cis effects, including variants in or near the IL6R (p = 1.8×10 -57), CCL4L1 (p = 3.9×10-21), IL18 (p = 6.8×10-13), LPA (p = 4.4×10-10), GGT1 (p = 1.5×10-7), SHBG (p = 3.1×10-7), CRP (p = 6.4×10-6) and IL1RN (p = 7.3×10-6) genes, all associated with their respective protein products with effect sizes ranging from 0.19 to 0.69 standard deviations per allele. Mechanisms implicated include altered rates of cleavage of bound to unbound soluble receptor (IL6R), altered secretion rates of different sized proteins (LPA), variation in gene copy number (CCL4L1) and altered transcription (GGT1). We identified one novel trans effect that was an association between ABO blood group and tumour necrosis factor alpha (TNF-alpha) levels (p = 6.8×10-40), but this finding was not present when TNF-alpha was measured using a different assay , or in a second study, suggesting an assay-specific association. Our results show that protein levels share some of the features of the genetics of gene expression. These include the presence of strong genetic effects in cis locations. The identification of protein quantitative trait loci (pQTLs) may be a powerful complementary method of improving our understanding of disease pathways. © 2008 Melzer et al

Robotic milking technologies and renegotiating situated ethical relationships on UK dairy farms

Robotic or automatic milking systems (AMS) are novel technologies that take over the labor of dairy farming and reduce the need for human-animal interactions. Because robotic milking involves the replacement of 'conventional' twice-a-day milking managed by people with a system that supposedly allows cows the freedom to be milked automatically whenever they choose, some claim robotic milking has health and welfare benefits for cows, increases productivity, and has lifestyle advantages for dairy farmers. This paper examines how established ethical relations on dairy farms are unsettled by the intervention of a radically different technology such as AMS. The renegotiation of ethical relationships is thus an important dimension of how the actors involved are re-assembled around a new technology. The paper draws on in-depth research on UK dairy farms comparing those using conventional milking technologies with those using AMS. We explore the situated ethical relations that are negotiated in practice, focusing on the contingent and complex nature of human-animal-technology interactions. We show that ethical relations are situated and emergent, and that as the identities, roles, and subjectivities of humans and animals are unsettled through the intervention of a new technology, the ethical relations also shift. © 2013 Springer Science+Business Media Dordrecht

Focal brain trauma in the cryogenic lesion model in mice

The method to induce unilateral cryogenic lesions was first described in 1958 by Klatzo. We describe here an adaptation of this model that allows reliable measurement of lesion volume and vasogenic edema by 2, 3, 5-triphenyltetrazolium chloride-staining and Evans blue extravasation in mice. A copper or aluminium cylinder with a tip diameter of 2.5 mm is cooled with liquid nitrogen and placed on the exposed skull bone over the parietal cortex (coordinates from bregma: 1.5 mm posterior, 1.5 mm lateral). The tip diameter and the contact time between the tip and the parietal skull determine the extent of cryolesion. Due to an early damage of the blood brain barrier, the cryogenic cortical injury is characterized by vasogenic edema, marked brain swelling, and inflammation. The lesion grows during the first 24 hours, a process involving complex interactions between endothelial cells, immune cells, cerebral blood flow, and the intracranial pressure. These contribute substantially to the damage from the initial injury. The major advantage of the cryogenic lesion model is the circumscribed and highly reproducible lesion size and location

A graphical model approach for inferring large-scale networks integrating gene expression and genetic polymorphism

<p>Abstract</p> <p>Background</p> <p>Graphical models (e.g., Bayesian networks) have been used frequently to describe complex interaction patterns and dependent structures among genes and other phenotypes. Estimation of such networks has been a challenging problem when the genes considered greatly outnumber the samples, and the situation is exacerbated when one wishes to consider the impact of polymorphisms (SNPs) in genes.</p> <p>Results</p> <p>Here we describe a multistep approach to infer a gene-SNP network from gene expression and genotyped SNP data. Our approach is based on 1) construction of a graphical Gaussian model (GGM) based on small sample estimation of partial correlation and false-discovery rate multiple testing; 2) extraction of a subnetwork of genes directly linked to a target candidate gene of interest; 3) identification of cis-acting regulatory variants for the genes composing the subnetwork; and 4) evaluating the identified cis-acting variants for trans-acting regulatory effects of the target candidate gene. This approach identifies significant gene-gene and gene-SNP associations not solely on the basis of gene co-expression but rather through whole-network modeling. We demonstrate the method by building two complex gene-SNP networks around Interferon Receptor 12B2 (IL12RB2) and Interleukin 1B (IL1B), two biologic candidates in asthma pathogenesis, using 534,290 genotyped variants and gene expression data on 22,177 genes from total RNA derived from peripheral blood CD4+ lymphocytes from 154 asthmatics.</p> <p>Conclusion</p> <p>Our results suggest that graphical models based on integrative genomic data are computationally efficient, work well with small samples, and can describe complex interactions among genes and polymorphisms that could not be identified by pair-wise association testing.</p

Complex exon-intron marking by histone modifications is not determined solely by nucleosome distribution

It has recently been shown that nucleosome distribution, histone modifications and RNA polymerase II (Pol II) occupancy show preferential association with exons (“exon-intron marking”), linking chromatin structure and function to co-transcriptional splicing in a variety of eukaryotes. Previous ChIP-sequencing studies suggested that these marking patterns reflect the nucleosomal landscape. By analyzing ChIP-chip datasets across the human genome in three cell types, we have found that this marking system is far more complex than previously observed. We show here that a range of histone modifications and Pol II are preferentially associated with exons. However, there is noticeable cell-type specificity in the degree of exon marking by histone modifications and, surprisingly, this is also reflected in some histone modifications patterns showing biases towards introns. Exon-intron marking is laid down in the absence of transcription on silent genes, with some marking biases changing or becoming reversed for genes expressed at different levels. Furthermore, the relationship of this marking system with splicing is not simple, with only some histone modifications reflecting exon usage/inclusion, while others mirror patterns of exon exclusion. By examining nucleosomal distributions in all three cell types, we demonstrate that these histone modification patterns cannot solely be accounted for by differences in nucleosome levels between exons and introns. In addition, because of inherent differences between ChIP-chip array and ChIP-sequencing approaches, these platforms report different nucleosome distribution patterns across the human genome. Our findings confound existing views and point to active cellular mechanisms which dynamically regulate histone modification levels and account for exon-intron marking. We believe that these histone modification patterns provide links between chromatin accessibility, Pol II movement and co-transcriptional splicing

Candidate Causal Regulatory Effects by Integration of Expression QTLs with Complex Trait Genetic Associations

The recent success of genome-wide association studies (GWAS) is now followed by the challenge to determine how the reported susceptibility variants mediate complex traits and diseases. Expression quantitative trait loci (eQTLs) have been implicated in disease associations through overlaps between eQTLs and GWAS signals. However, the abundance of eQTLs and the strong correlation structure (LD) in the genome make it likely that some of these overlaps are coincidental and not driven by the same functional variants. In the present study, we propose an empirical methodology, which we call Regulatory Trait Concordance (RTC) that accounts for local LD structure and integrates eQTLs and GWAS results in order to reveal the subset of association signals that are due to cis eQTLs. We simulate genomic regions of various LD patterns with both a single or two causal variants and show that our score outperforms SNP correlation metrics, be they statistical (r2) or historical (D'). Following the observation of a significant abundance of regulatory signals among currently published GWAS loci, we apply our method with the goal to prioritize relevant genes for each of the respective complex traits. We detect several potential disease-causing regulatory effects, with a strong enrichment for immunity-related conditions, consistent with the nature of the cell line tested (LCLs). Furthermore, we present an extension of the method in trans, where interrogating the whole genome for downstream effects of the disease variant can be informative regarding its unknown primary biological effect. We conclude that integrating cellular phenotype associations with organismal complex traits will facilitate the biological interpretation of the genetic effects on these traits