Code génétique | EPFL Graph Search

225px|droite|vignette| À gauche, un brin d'ARNm avec sa séquence de bases nucléiques ; à droite, les codons correspondants, chacun spécifiant un acide aminé protéinogène. Le code génétique est l'ensemble des règles permettant de traduire les informations contenues dans le génome des cellules vivantes afin de synthétiser les protéines. Au sens large, il établit la correspondance entre le génotype et le phénotype d'un organisme. Ce code repose notamment sur la correspondance entre, d'une part, des triplets de nucléotides, appelés codons, sur l'ARN messager et, d'autre part, les acides aminés protéinogènes incorporés dans les protéines synthétisées lors de la phase de traduction de l'ARN messager par les ribosomes. À quelques exceptions près, chaque codon correspond à un seul et unique acide aminé protéinogène. Dans la mesure où l'information génétique est codée exactement de la même façon dans les gènes de la très grande majorité des différentes espèces vivantes, ce code génétique spéc

Host genetic predictors of the kynurenine pathway of tryptophan catabolism among treated HIV-infected Ugandans

Yunfei Huang

Objective: Plasma kynurenine/tryptophan ratio, a biomarker of indoleamine 2,3-dioxygenase-1 (IDO) activity, is a strong independent predictor of mortality in HIV-infected Ugandans initiating antiretroviral therapy (ART) and may play a key role in HIV pathogenesis. We performed a genome-wide study to identify potential host genetic determinants of kynurenine/tryptophan ratio in HIV-infected ART-suppressed Ugandans. Design/methods: We performed genome-wide and exome array genotyping and measured plasma kynurenine/tryptophan ratio during the initial 6-12 months of suppressive ART in Ugandans. We evaluated more than 16 million single nucleotide polymorphisms in association with log(10) kynurenine/tryptophan ratio using linear mixed models adjusted for cohort, sex, pregnancy, and ancestry. Results: Among 597 Ugandans, 62% were woman, median age was 35, median baseline CD4(+) cell count was 135 cells/mu l, and median baseline HIV-1 RNA was 5.1 log(10) copies/ml. Several polymorphisms in candidate genes TNF, IFNGR1, and TLR4 were associated with log(10) kynurenine/tryptophan ratio (P < 5.0 x 10(-5)). An intergenic polymorphism between CSPG5 and ELP6 was genome-wide significant, whereas several others exhibited suggestive associations (P < 5.0 x 10(-7)), including genes encoding protein tyrosine phosphatases (PTPRM and PTPRN2) and the vitamin D metabolism gene, CYP24A1. Several of these single nucleotide polymorphisms were associated with markers of inflammation, coagulation, and monocyte activation, but did not replicate in a small US cohort (N = 262; 33% African-American). Conclusion: Our findings highlight a potentially important role of IFN-gamma, TNF-alpha, and Toll-like receptor signaling in determining IDO activity and subsequent mortality risk in HIV-infected ART-suppressed Ugandans. These results also identify potential novel pathways involved in IDO immunoregulation. Further studies are needed to confirm these findings in treated HIV-infected populations. Copyright (C) 2016 Wolters Kluwer Health, Inc. All rights reserved.

Lippincott Williams & Wilkins2016

Genetic, metabolic, and molecular insights into the diverse outcomes of diet-induced obesity in mouse

Alexis Maximilien Bachmann

Overweight and obesity are increasingly common public health issues worldwide, leading to a wide range of diseases from metabolic syndrome to steatohepatitis and cardiovascular diseases. While the increase in the prevalence of obesity is partly attributable to changes in lifestyle (i.e. increased sedentarity and changes in eating behaviour), the metabolic and clinical impacts of these obesogenic conditions varies between sexes and genetic backgrounds. The conception of personalised treatments of obesity and its complications require a thorough understanding of the diversity of responses to environmental stimuli such as high-fat diet intake. In this thesis, by analysing nine genetically diverse mouse strains, we show that much like humans, mice respond to high-fat diet in a genetic- and sex-dependent manner. Physiological and molecular responses to high-fat diet are associated with the expression of genes involved in immunity and mitochondrial function. Finally, we find that mitochondrial function and mitochondrial supercomplex assembly may explain part of the diversity of physiological responses. By exploring the complex interactions between genetics and metabolic phenotypes via gene expression and molecular traits, we shed light on the importance of genetic background and sex in determining metabolic outcomes. In addition to providing the community with an extensive resource for optimizing future experiments, this work serves as an exemplary design for more generalizable translational studies.

EPFL2021

A Systems Approach to Identify Genetic and Environmental Regulators of Metabolism

Evan Graehl Williams

For more than a century, it has been recognized that our genetic inheritance and our environment interact to shape who we are and how we act (nature vs. nurture), and the study of genetics has allowed us to explain why traits can vary dramatically between individuals (i.e. trait variance), and yet often be strongly shared within families (i.e. trait heritability). Scientists, statisticians, and physicians can calculate heritability, and can observe how both genes and environment influence health, but relatively few interventions or treatments have stemmed from this expanded knowledge. In large part this is because testing biomedical hypotheses is difficult, because we are all so different, and because environmental factors are so hard to control. In 2001 the first (nearly) complete DNA sequence of the human genome was generated after years of technical development and brute force effort. This accomplishment has ushered in a new field of personalized medicine, as prevention and treatment can both be tailored based on genome and environment. Unfortunately, the science of genomic sequencing has greatly outpaced our ability to actually understand the genetic code, and it remains difficult to make accurate predictions of an individual's characteristics and susceptibilities except in a few clear-cut cases such as eye color or risk of Huntington's disease. There are several reasons for this disconnect: (1) many traits and diseases are driven by complex interactions among environmental causes and genetic risks, (2) there are many aspects of genetics which we do not fully understand and that cannot be easily observed (e.g. non-coding RNAs, epigenetic modifications, and complex metagenomic cohorts that we all carry), and (3) gene products (such as mRNA and protein) change dramatically over time and across cell types in complex and even unpredictable ways. Today, myriad developments promise to improve our capacity to predict resistance and susceptibility to diseases based on our individual genome. This technical capability has also provided new avenues for developing therapeutic agents and/or lifestyle changes. However, no matter our developments in scientific understanding, personalized medicine will never allow perfect predictionâenvironmental influences and personal choices will always affect our health, and medical treatments will still require information gained from standard medical checkups. Rather than perfect prediction, personalized medicine will instead provide more accurate predictions on health than those previously possible. Thus, those at high risk for (e.g.) diabetes will know better to watch their diet, while those at low risk should remember that biology is never completely predictable. In this thesis, I have analyzed 45 strains of mice from a genetic reference population called the BXD with the goal of identifying major gene regulators of metabolic phenotypes, such as exercise capacity and glucose response. Each member of this BXD family, which contains ~150 distinct but related lines (or strains), has a unique genetic makeup that has been fixed by inbreeding. Each family member is thus available as an unlimited supply of identical twins which may be studied over years and among laboratories. With this population, it is possible to both (1) test what would occur to a single individual in different environmental conditions and (2) analyze how much environmental influences vary across genetically-diverse phenotypes.