Central role of Ifh1p–Fhl1p interaction in the synthesis of yeast ribosomal proteins

The 138 genes encoding the 79 ribosomal proteins (RPs) of Saccharomyces cerevisiae form the tightest cluster of coordinately regulated genes in nearly all transcriptome experiments. The basis for this observation remains unknown. We now provide evidence that two factors, Fhl1p and Ifh1p, are key players in the transcription of RP genes. Both are found at transcribing RP genes in vivo. Ifh1p, but not Fhl1p, leaves the RP genes when transcription is repressed. The occupancy of the RP genes by Ifh1p depends on its interaction with the phospho-peptide recognizing forkhead-associated domain of Fhl1p. Disruption of this interaction is severely deleterious to ribosome synthesis and cell growth. Loss of functional Fhl1p leads to cells that have only 20% the normal amount of RNA and that synthesize ribosomes at only 5–10% the normal rate. Homeostatic mechanisms within the cell respond by reducing the transcription of rRNA to match the output of RPs, and by reducing the global transcription of mRNA to match the capacity of the translational apparatus

Evaluation of a 7-gene genetic profile for athletic endurance phenotype in ironman championship triathletes

© 2015 Grealy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Polygenic profiling has been proposed for elite endurance performance, using an additive model determining the proportion of optimal alleles in endurance athletes. To investigate this model's utility for elite triathletes, we genotyped seven polymorphisms previously associated with an endurance polygenic profile (ACE Ins/Del, ACTN3 Arg577Ter, AMPD1 Gln12Ter, CKMM 1170bp/985+185bp, HFEHis63Asp, GDF8 Lys153Arg and PPARGC1A Gly482Ser) in a cohort of 196 elite athletes who participated in the 2008 Kona Ironman championship triathlon. Mean performance time (PT) was not significantly different in individual marker analysis. Age, sex, and continent of origin had a significant influence on PT and were adjusted for. Only the AMPD1 endurance-optimal Gln allele was found to be significantly associated with an improvement in PT (model p = 5.79 × 10- 17 , AMPD1 genotype p = 0.01). Individual genotypes were combined into a total genotype score (TGS);TGS distribution ranged from 28.6 to 92.9, concordant with prior studies in endurance athletes (mean±SD: 60.75±12.95). TGS distribution was shifted toward higher TGS in the top 10% of athletes, though the mean TGS was not significantly different (p = 0.164) and not significantly associated with PT even when adjusted for age, sex, and origin. Receiver operating characteristic curve analysis determined that TGS alone could not significantly predict athlete finishing time with discriminating sensitivity and specificity for three outcomes (less than median PT, less than mean PT, or in the top 10%), though models with the age, sex, continent of origin, and either TGS orAMPD1 genotype could. These results suggest three things: that more sophisticated genetic models may be necessary to accurately predict athlete finishing time in endurance events; that non-genetic factors such as training are hugely influential and should be included in genetic analyses to prevent confounding; and that large collaborations may be necessary to obtain sufficient sample sizes for powerful and complex analyses of endurance performance