Mapping liver fat female-dependent quantitative trait loci in collaborative cross mice.

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in the western world, with spectrum from simple steatosis to non-alcoholic steatohepatitis, which can progress to cirrhosis. NAFLD developments are known to be affected by host genetic background. Herein we emphasize the power of collaborative cross (CC) mouse for dissecting this complex trait and revealing quantitative trait loci (QTL) controlling hepatic fat accumulation in mice. 168 female and 338 male mice from 24 and 37 CC lines, respectively, of 18-20 weeks old, maintained on standard rodent diet, since weaning. Hepatic fat content was assessed, using dual DEXA scan in the liver. Using the available high-density genotype markers of the CC line, QTL mapping associated with percentage liver fat accumulation was performed. Our results revealed significant fatty liver accumulation QTL that were specifically, mapped in females. Two significant QTLs on chromosomes 17 and 18, with genomic intervals 3 and 2 Mb, respectively, were mapped. A third QTL, with a less significant P value, was mapped to chromosome 4, with genomic interval of 2 Mb. These QTLs were named Flal1-Flal3, referring to Fatty Liver Accumulation Locus 1-3, for the QTLs on chromosomes 17, 18, and 4, respectively. Unfortunately, no QTL was mapped with males. Searching the mouse genome database suggested several candidate genes involved in hepatic fat accumulation. Our results show that susceptibility to hepatic fat accumulations is a complex trait, controlled by multiple genetic factors in female mice, but not in male.

Dissecting dynamic genetic variation that controls temporal gene response in yeast.

Inter-individual variation in regulatory circuits controlling gene expression is a powerful source of functional information. The study of associations among genetic variants and gene expression provides important insights about cell circuitry but cannot specify whether and when potential variants dynamically alter their genetic effect during the course of response. Here we develop a computational procedure that captures temporal changes in genetic effects, and apply it to analyze transcription during inhibition of the TOR signaling pathway in segregating yeast cells. We found a high-order coordination of gene modules: sets of genes co-associated with the same genetic variant and sharing a common temporal genetic effect pattern. The temporal genetic effects of some modules represented a single state-transitioning pattern; for example, at 10-30 minutes following stimulation, genetic effects in the phosphate utilization module attained a characteristic transition to a new steady state. In contrast, another module showed an impulse pattern of genetic effects; for example, in the poor nitrogen sources utilization module, a spike up of a genetic effect at 10-20 minutes following stimulation reflected inter-individual variation in the timing (rather than magnitude) of response. Our analysis suggests that the same mechanism typically leads to both inter-individual variation and the temporal genetic effect pattern in a module. Our methodology provides a quantitative genetic approach to studying the molecular mechanisms that shape dynamic changes in transcriptional responses.

Codon usage bias in prokaryotic pyrimidine-ending codons is associated with the degeneracy of the encoded amino acids.

Synonymous codons are unevenly distributed among genes, a phenomenon termed codon usage bias. Understanding the patterns of codon bias and the forces shaping them is a major step towards elucidating the adaptive advantage codon choice can confer at the level of individual genes and organisms. Here, we perform a large-scale analysis to assess codon usage bias pattern of pyrimidine-ending codons in highly expressed genes in prokaryotes. We find a bias pattern linked to the degeneracy of the encoded amino acid. Specifically, we show that codon-pairs that encode two- and three-fold degenerate amino acids are biased towards the C-ending codon while codons encoding four-fold degenerate amino acids are biased towards the U-ending codon. This codon usage pattern is widespread in prokaryotes, and its strength is correlated with translational selection both within and between organisms. We show that this bias is associated with an improved correspondence with the tRNA pool, avoidance of mis-incorporation errors during translation and moderate stability of codon-anticodon interaction, all consistent with more efficient translation.

Significant common environmental effects on leukocyte subpopulations.

Major efforts are invested in the analysis of phenotypic variation in a population of individuals. While many of these studies focus on the genetic basis of phenotypic variation via measurements of DNA polymorphic sites, the environmental effects are still elusive. Here we propose a methodology, called CCCE ('Cell Composition Common Environment'), to identify environmental effects on the composition of immune cell functionalities. Specifically, CCCE is focused on the common experiences that are shared between siblings (the 'common environment'), designed to correct for cell subpopulation heterogeneity, and is based on a multicolor flow cytometry analysis across a large cohort of human monozygotic and dizygotic twins. We demonstrate that the CCCE methodology can provide insights on the relations between common environmental effects and the heterogenic functions of several immune cell types, such as NK cells effector functions and coagulation-related capabilities of monocytes. The software described in this article is available at http://csgi.tau.ac.il/CCCE.

Mapping novel genetic loci associated with female liver weight variations using Collaborative Cross mice.

BACKGROUND: Liver weight is a complex trait, controlled by polygenic factors and differs within populations. Dissecting the genetic architecture underlying these variations will facilitate the search for key role candidate genes involved directly in the hepatomegaly process and indirectly involved in related diseases etiology. METHODS: Liver weight of 506 mice generated from 39 different Collaborative Cross (CC) lines with both sexes at age 20 weeks old was determined using an electronic balance. Genomic DNA of the CC lines was genotyped with high-density single nucleotide polymorphic markers. RESULTS: Statistical analysis revealed a significant (P < 0.05) variation of liver weight between the CC lines, with broad sense heritability (H 2) of 0.32 and genetic coefficient of variation (CVG) of 0.28. Subsequently, quantitative trait locus (QTL) mapping was performed, and results showed a significant QTL only for females on chromosome 8 at genomic interval 88.61-93.38 Mb (4.77 Mb). Three suggestive QTL were mapped at chromosomes 4, 12 and 13. The four QTL were designated as LWL1-LWL4 referring to liver weight loci 1-4 on chromosomes 8, 4, 12 and 13, respectively. CONCLUSION: To our knowledge, this report presents, for the first time, the utilization of the CC for mapping QTL associated with baseline liver weight in mice. Our findings demonstrate that liver weight is a complex trait controlled by multiple genetic factors that differ significantly between sexes.

POEM: Identifying Joint Additive Effects on Regulatory Circuits.

MOTIVATION: Expression Quantitative Trait Locus (eQTL) mapping tackles the problem of identifying variation in DNA sequence that have an effect on the transcriptional regulatory network. Major computational efforts are aimed at characterizing the joint effects of several eQTLs acting in concert to govern the expression of the same genes. Yet, progress toward a comprehensive prediction of such joint effects is limited. For example, existing eQTL methods commonly discover interacting loci affecting the expression levels of a module of co-regulated genes. Such "modularization" approaches, however, are focused on epistatic relations and thus have limited utility for the case of additive (non-epistatic) effects. RESULTS: Here we present POEM (Pairwise effect On Expression Modules), a methodology for identifying pairwise eQTL effects on gene modules. POEM is specifically designed to achieve high performance in the case of additive joint effects. We applied POEM to transcription profiles measured in bone marrow-derived dendritic cells across a population of genotyped mice. Our study reveals widespread additive, trans-acting pairwise effects on gene modules, characterizes their organizational principles, and highlights high-order interconnections between modules within the immune signaling network. These analyses elucidate the central role of additive pairwise effect in regulatory circuits, and provide computational tools for future investigations into the interplay between eQTLs. AVAILABILITY: The software described in this article is available at csgi.tau.ac.il/POEM/.

Variation in global codon usage bias among prokaryotic organisms is associated with their lifestyles.

BACKGROUND: It is widely acknowledged that synonymous codons are used unevenly among genes in a genome. In organisms under translational selection, genes encoding highly expressed proteins are enriched with specific codons. This phenomenon, termed codon usage bias, is common to many organisms and has been recognized as influencing cellular fitness. This suggests that the global extent of codon usage bias of an organism might be associated with its phenotypic traits. RESULTS: To test this hypothesis we used a simple measure for assessing the extent of codon bias of an organism, and applied it to hundreds of sequenced prokaryotes. Our analysis revealed a large variability in this measure: there are organisms showing very high degrees of codon usage bias and organisms exhibiting almost no differential use of synonymous codons among different genes. Remarkably, we found that the extent of codon usage bias corresponds to the lifestyle of the organism. Especially, organisms able to live in a wide range of habitats exhibit high extents of codon usage bias, consistent with their need to adapt efficiently to different environments. Pathogenic prokaryotes also demonstrate higher extents of codon usage bias than non-pathogenic prokaryotes, in accord with the multiple environments that many pathogens occupy. Our results show that the previously observed correlation between growth rate and metabolic variability is attributed to their individual associations with codon usage bias. CONCLUSIONS: Our results suggest that the extent of codon usage bias of an organism plays a role in the adaptation of prokaryotes to their environments.