Liver-specific expression of the agouti gene in transgenic mice promotes liver carcinogenesis in the absence of obesity and diabetes

BACKGROUND: The agouti protein is a paracrine factor that is normally present in the skin of many species of mammals. Agouti regulates the switch between black and yellow hair pigmentation by signalling through the melanocortin 1 receptor (Mc1r) on melanocytes. Lethal yellow (A(y)) and viable yellow (A(vy)) are dominant regulatory mutations in the mouse agouti gene that cause the wild-type protein to be produced at abnormally high levels throughout the body. Mice harboring these mutations exhibit a pleiotropic syndrome characterized by yellow coat color, obesity, hyperglycemia, hyperinsulinemia, and increased susceptibility to hyperplasia and carcinogenesis in numerous tissues, including the liver. The goal of this research was to determine if ectopic expression of the agouti gene in the liver alone is sufficient to recapitulate any aspect of this syndrome. For this purpose, we generated lines of transgenic mice expressing high levels of agouti in the liver under the regulatory control of the albumin promoter. Expression levels of the agouti transgene in the liver were quantified by Northern blot analysis. Functional agouti protein in the liver of transgenic mice was assayed by its ability to inhibit binding of the α-melanocyte stimulating hormone (αMSH) to the Mc1r. Body weight, plasma insulin and blood glucose levels were analyzed in control and transgenic mice. Control and transgenic male mice were given a single intraperitoneal injection (10 mg/kg) of the hepatocellular carcinogen, diethylnitrosamine (DEN), at 15 days of age. Mice were euthanized at 36 or 40 weeks after DEN injection and the number of tumors per liver and total liver weights were recorded. RESULTS: The albumin-agouti transgene was expressed at high levels in the livers of mice and produced a functional agouti protein. Albumin-agouti transgenic mice had normal body weights and normal levels of blood glucose and plasma insulin, but responded to chemical initiation of the liver with an increased number of liver tumors compared to non-transgenic control mice. CONCLUSIONS: The data demonstrate that liver-specific expression of the agouti gene is not sufficient to induce obesity or diabetes, but, in the absence of these factors, agouti continues to promote hepatocellular carcinogenesis

Environmental and Molecular Mutagenesis Meeting Report Assessing Human Germ-Cell Mutagenesis in the Post-Genome Era: A Celebration of the Legacy of William Lawson (Bill) Russell

ABSTRACT Although numerous germ-cell mutagens have been identified in animal model systems, to date, no human germ-cell mutagens have been confirmed. Because the genomic integrity of our germ cells is essential for the continuation of the human species, a resolution of this enduring conundrum is needed. To facilitate such a resolution, we organized a workshop at The Jackson Laboratory in Bar Harbor, Maine on September [28][29][30] 2004. This interactive workshop brought together scientists from a wide range of disciplines to assess the applicability of emerging molecular methods for genomic analysis to the field of human germ-cell mutagenesis. Participants recommended that focused, coordinated human germ-cell mutation studies be conducted in relation to important societal exposures. Because cancer survivors represent a unique cohort with well-defined exposures, there was a consensus that studies should be designed to assess the mutational impact on children born to parents who had received certain types of mutagenic cancer chemotherapy prior to conceiving their children. Within this high-risk cohort, parents and children could be evaluated for inherited changes in (a) gene sequences and chromosomal structure, (b) repeat sequences and minisatellite regions, and (c) global gene expression and chromatin. Participants also recommended studies to examine trans-generational effects in humans involving mechanisms such as changes in imprinting and methylation patterns, expansion of nucleotide repeats, or induction of mitochondrial DNA mutations. Workshop participants advocated establishment of a bio-bank of human tissue samples that could be used to conduct a multiple-endpoint, comprehensive, and collaborative effort to detect exposure-induced heritable alterations in the human genome. Appropriate animal models of human germ-cell mutagenesis should be used in parallel with human studies to provide insights into the mechanisms of mammalian germ-cell mutagenesis. Finally, participants recommended that 4 scientific specialty groups be convened to address specific questions regarding the potential germ-cell mutagenicity of environmental, occupational, and lifestyle exposures. Strong support from relevant funding agencies and engagement of scientists outside the fields of genomics and germ-cell mutagenesis will be required to launch a full-scale assault on some of the most pressing and enduring questions in environmental mutagenesis: Do human germ-cell mutagens exist, what risk do they pose to future generations, and are some parents at higher risk than others for acquiring and transmitting germ-cell mutations?

A Leveraged Signal-to-Noise Ratio (LSTNR) Method to Extract Differentially Expressed Genes and Multivariate Patterns of Expression From Noisy and Low-Replication RNAseq Data

To life scientists, one important feature offered by RNAseq, a next-generation sequencing tool used to estimate changes in gene expression levels, lies in its unprecedented resolution. It can score countable differences in transcript numbers among thousands of genes and between experimental groups, all at once. However, its high cost limits experimental designs to very small sample sizes, usually N = 3, which often results in statistically underpowered analysis and poor reproducibility. All these issues are compounded by the presence of experimental noise, which is harder to distinguish from instrumental error when sample sizes are limiting (e.g., small-budget pilot tests), experimental populations exhibit biologically heterogeneous or diffuse expression phenotypes (e.g., patient samples), or when discriminating among transcriptional signatures of closely related experimental conditions (e.g., toxicological modes of action, or MOAs). Here, we present a leveraged signal-to-noise ratio (LSTNR) thresholding method, founded on generalized linear modeling (GLM) of aligned read detection limits to extract differentially expressed genes (DEGs) from noisy low-replication RNAseq data. The LSTNR method uses an agnostic independent filtering strategy to define the dynamic range of detected aggregate read counts per gene, and assigns statistical weights that prioritize genes with better sequencing resolution in differential expression analyses. To assess its performance, we implemented the LSTNR method to analyze three separate datasets: first, using a systematically noisy in silico dataset, we demonstrated that LSTNR can extract pre-designed patterns of expression and discriminate between “noise” and “true” differentially expressed pseudogenes at a 100% success rate; then, we illustrated how the LSTNR method can assign patient-derived breast cancer specimens correctly to one out of their four reported molecular subtypes (luminal A, luminal B, Her2-enriched and basal-like); and last, we showed the ability to retrieve five different modes of action (MOA) elicited in livers of rats exposed to three toxicants under three nutritional routes by using the LSTNR method. By combining differential measurements with resolving power to detect DEGs, the LSTNR method offers an alternative approach to interrogate noisy and low-replication RNAseq datasets, which handles multiple biological conditions at once, and defines benchmarks to validate RNAseq experiments with standard benchtop assays

Table_1_A Leveraged Signal-to-Noise Ratio (LSTNR) Method to Extract Differentially Expressed Genes and Multivariate Patterns of Expression From Noisy and Low-Replication RNAseq Data.xlsx

<p>To life scientists, one important feature offered by RNAseq, a next-generation sequencing tool used to estimate changes in gene expression levels, lies in its unprecedented resolution. It can score countable differences in transcript numbers among thousands of genes and between experimental groups, all at once. However, its high cost limits experimental designs to very small sample sizes, usually N = 3, which often results in statistically underpowered analysis and poor reproducibility. All these issues are compounded by the presence of experimental noise, which is harder to distinguish from instrumental error when sample sizes are limiting (e.g., small-budget pilot tests), experimental populations exhibit biologically heterogeneous or diffuse expression phenotypes (e.g., patient samples), or when discriminating among transcriptional signatures of closely related experimental conditions (e.g., toxicological modes of action, or MOAs). Here, we present a leveraged signal-to-noise ratio (LSTNR) thresholding method, founded on generalized linear modeling (GLM) of aligned read detection limits to extract differentially expressed genes (DEGs) from noisy low-replication RNAseq data. The LSTNR method uses an agnostic independent filtering strategy to define the dynamic range of detected aggregate read counts per gene, and assigns statistical weights that prioritize genes with better sequencing resolution in differential expression analyses. To assess its performance, we implemented the LSTNR method to analyze three separate datasets: first, using a systematically noisy in silico dataset, we demonstrated that LSTNR can extract pre-designed patterns of expression and discriminate between “noise” and “true” differentially expressed pseudogenes at a 100% success rate; then, we illustrated how the LSTNR method can assign patient-derived breast cancer specimens correctly to one out of their four reported molecular subtypes (luminal A, luminal B, Her2-enriched and basal-like); and last, we showed the ability to retrieve five different modes of action (MOA) elicited in livers of rats exposed to three toxicants under three nutritional routes by using the LSTNR method. By combining differential measurements with resolving power to detect DEGs, the LSTNR method offers an alternative approach to interrogate noisy and low-replication RNAseq datasets, which handles multiple biological conditions at once, and defines benchmarks to validate RNAseq experiments with standard benchtop assays.</p

Molecular and phenotypic analysis of 25 recessive, homozygous-viable alleles at the mouse agouti locus.

Agouti is a paracrine-acting, transient antagonist of melanocortin 1 receptors that specifies the subapical band of yellow on otherwise black hairs of the wild-type coat. To better understand both agouti structure/function and the germline damage caused by chemicals and radiation, an allelic series of 25 recessive, homozygous-viable agouti mutations generated in specific-locus tests were characterized. Visual inspection of fur, augmented by quantifiable chemical analysis of hair melanins, suggested four phenotypic categories (mild, moderate, umbrous-like, severe) for the 18 hypomorphs and a single category for the 7 amorphs (null). Molecular analysis indicated protein-coding alterations in 8 hypomorphs and 6 amorphs, with mild-moderate phenotypes correlating with signal peptide or basic domain mutations, and more devastating phenotypes resulting from C-terminal lesions. Ten hypomorphs and one null demonstrated wild-type coding potential, suggesting that they contain mutations elsewhere in the > or = 125-kb agouti locus that either reduce the level or alter the temporal/spatial distribution of agouti transcripts. Beyond the notable contributions to the field of mouse germ cell mutagenesis, analysis of this allelic series illustrates that complete abrogation of agouti function in vivo occurs most often through protein-coding lesions, whereas partial loss of function occurs slightly more frequently at the level of gene expression control

Mitochondrial nicotinamide adenine dinucleotide reduced (NADH) oxidation links the tricarboxylic acid (TCA) cycle with methionine metabolism and nuclear DNA methylation

<div><p>Mitochondrial function affects many aspects of cellular physiology, and, most recently, its role in epigenetics has been reported. Mechanistically, how mitochondrial function alters DNA methylation patterns in the nucleus remains ill defined. Using a cell culture model of induced mitochondrial DNA (mtDNA) depletion, in this study we show that progressive mitochondrial dysfunction leads to an early transcriptional and metabolic program centered on the metabolism of various amino acids, including those involved in the methionine cycle. We find that this program also increases DNA methylation, which occurs primarily in the genes that are differentially expressed. Maintenance of mitochondrial nicotinamide adenine dinucleotide reduced (NADH) oxidation in the context of mtDNA loss rescues methionine salvage and polyamine synthesis and prevents changes in DNA methylation and gene expression but does not affect serine/folate metabolism or transsulfuration. This work provides a novel mechanistic link between mitochondrial function and epigenetic regulation of gene expression that involves polyamine and methionine metabolism responding to changes in the tricarboxylic acid (TCA) cycle. Given the implications of these findings, future studies across different physiological contexts and in vivo are warranted.</p></div