Genetic Regulation of NKT Cell Function

NKT cells are specialized T cells that play important roles in the host immune response to bacteria and viruses. NKT cells produce a wide variety of cytokines and chemokines after being activated by glycolipids such as α-galactosylceramide (αGalCer). Previous work suggested that the ability of NKT cells to be activated by aGalCer mapped to a genetic region encompassing a gene family (Slam genes) that is known to be important in NKT cell development, but the exact gene in this region which regulates NKT cells is unknown. This study utilizes a panel of C57BL/6 (B6) mice containing different regions of chromosome 1 derived from 129X1/SvJ mice (B6.129 congenics) to identify candidate genes regulating NKT cell function by positionally mapping the genes within this locus. We assessed NKT cell function in B6.129c2 (C2), B6.129c3 (C3), and B6.129c4 (C4) mice, which contain 129 intervals ranging from 0.1-1 megabase pairs (Mbp). To assess NKT cell function, we injected mice with αGalCer, which specifically activates NKT cells. Flow cytometry was utilized to determine NKT cell IL-4, TNF, and IFN-g expression on a per cell basis and ELISA assays were conducted to observe the overall magnitude of the NKT cell response. There was a significant reduction in the TNF, IL-4, and IFNγ production in all congenic mice as compared to B6 controls. These data suggested that the NKT cell response to αGalCer mapped to a 0.1 Mbp region on chromosome 1 (the C3 interval), which excluded Slam genes as potential genes regulating these NKT cell functions. Possible candidate genes of interest in this locus are ApoA2, which encodes a protein involved in lipid transport, and Fcer1g, which encodes a protein that has recently been implicated in the development of different NKT cell subsets

The Socio-genetic marginalization in Asia programme (SMAP)

SMAP, the Socio-genetic Marginalization in Asia Programme, which started off in August 2004, is a research programme set up with the support of the Netherlands Science Organisation (NWO), IIAS, and the Amsterdam School for Social Science Research (ASSR). Exploring cultural, social and economic aspects of the role of genetic technologies played in the area of state organisation, population policies, health care systems and research regulation in China, India and Japan, SMAP is expected to shed light on how differences in the application of modern genetic technologies generate different practices. The programme focuses on: (I) the ways in which (universal) regulation for genetic sampling by international companies and universities leads to disputable research practices among vulnerable populations; (II) how bioethical differences between healthcare systems are expressed in the different meanings allocated to concepts, such as informed consent, health, and family values; and, (III) the consequences of development priorities and practices of genetic screening for the livelihood and identities of diverging social groups

Computational mechanisms in genetic regulation by RNA

The evolution of the genome has led to very sophisticated and complex regulation. Because of the abundance of non-coding RNA (ncRNA) in the cell, different species will promiscuously associate with each other, suggesting collective dynamics similar to artificial neural networks. Here we present a simple mechanism allowing ncRNA to perform computations equivalent to neural network algorithms such as Boltzmann machines and the Hopfield model. The quantities analogous to the neural couplings are the equilibrium constants between different RNA species. The relatively rapid equilibration of RNA binding and unbinding is regulated by a slower process that degrades and creates new RNA. The model requires that the creation rate for each species be an increasing function of the ratio of total to unbound RNA. Similar mechanisms have already been found to exist experimentally for ncRNA regulation. With the overall concentration of RNA regulated, equilibrium constants can be chosen to store many different patterns, or many different input-output relations. The network is also quite insensitive to random mutations in equilibrium constants. Therefore one expects that this kind of mechanism will have a much higher mutation rate than ones typically regarded as being under evolutionary constraint.Comment: 18 pages, 10 figure

Genetic regulation of mouse liver metabolite levels.

We profiled and analyzed 283 metabolites representing eight major classes of molecules including Lipids, Carbohydrates, Amino Acids, Peptides, Xenobiotics, Vitamins and Cofactors, Energy Metabolism, and Nucleotides in mouse liver of 104 inbred and recombinant inbred strains. We find that metabolites exhibit a wide range of variation, as has been previously observed with metabolites in blood serum. Using genome-wide association analysis, we mapped 40% of the quantified metabolites to at least one locus in the genome and for 75% of the loci mapped we identified at least one candidate gene by local expression QTL analysis of the transcripts. Moreover, we validated 2 of 3 of the significant loci examined by adenoviral overexpression of the genes in mice. In our GWAS results, we find that at significant loci the peak markers explained on average between 20 and 40% of variation in the metabolites. Moreover, 39% of loci found to be regulating liver metabolites in mice were also found in human GWAS results for serum metabolites, providing support for similarity in genetic regulation of metabolites between mice and human. We also integrated the metabolomic data with transcriptomic and clinical phenotypic data to evaluate the extent of co-variation across various biological scales

Genetic insights on sleep schedules: this time, it's PERsonal.

The study of circadian rhythms is emerging as a fruitful opportunity for understanding cellular mechanisms that govern human physiology and behavior, fueled by evidence directly linking sleep disorders to genetic mutations affecting circadian molecular pathways. Familial advanced sleep-phase disorder (FASPD) is the first recognized Mendelian circadian rhythm trait, and affected individuals exhibit exceptionally early sleep-wake onset due to altered post-translational regulation of period homolog 2 (PER2). Behavioral and cellular circadian rhythms are analogously affected because the circadian period length of behavior is reduced in the absence of environmental time cues, and cycle duration of the molecular clock is likewise shortened. In light of these findings, we review the PER2 dynamics in the context of circadian regulation to reveal the mechanism of sleep-schedule modulation. Understanding PER2 regulation and functionality may shed new light on how our genetic composition can influence our sleep-wake behaviors

Meiotic DSB patterning: A multifaceted process

Meiosis is a specialized two-step cell division responsible for genome haploidization and the generation of genetic diversity during gametogenesis. An integral and distinctive feature of the meiotic program is the evolutionarily conserved initiation of homologous recombination (HR) by the developmentally programmed induction of DNA double-strand breaks (DSBs). The inherently dangerous but essential act of DSB formation is subject to multiple forms of stringent and self-corrective regulation that collectively ensure fruitful and appropriate levels of genetic exchange without risk to cellular survival. Within this article we focus upon an emerging element of this control—spatial regulation—detailing recent advances made in understanding how DSBs are evenly distributed across the genome, and present a unified view of the underlying patterning mechanisms employed

Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53

Background: The availability of various "omics" datasets creates a prospect of performing the study of genome-wide genetic regulatory networks. However, one of the major challenges of using mathematical models to infer genetic regulation from microarray datasets is the lack of information for protein concentrations and activities. Most of the previous researches were based on an assumption that the mRNA levels of a gene are consistent with its protein activities, though it is not always the case. Therefore, a more sophisticated modelling framework together with the corresponding inference methods is needed to accurately estimate genetic regulation from "omics" datasets. Results: This work developed a novel approach, which is based on a nonlinear mathematical model, to infer genetic regulation from microarray gene expression data. By using the p53 network as a test system, we used the nonlinear model to estimate the activities of transcription factor (TF) p53 from the expression levels of its target genes, and to identify the activation/inhibition status of p53 to its target genes. The predicted top 317 putative p53 target genes were supported by DNA sequence analysis. A comparison between our prediction and the other published predictions of p53 targets suggests that most of putative p53 targets may share a common depleted or enriched sequence signal on their upstream non-coding region. Conclusions: The proposed quantitative model can not only be used to infer the regulatory relationship between TF and its down-stream genes, but also be applied to estimate the protein activities of TF from the expression levels of its target genes