A Systems Approach to Identify Genetic and Environmental Regulators of Metabolism

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.

Privacy-Enhancing Technologies for Medical and Genomic Data: From Theory to Practice

Jean Louis Raisaro

The impressive technological advances in genomic analysis and the significant drop in the cost of genome sequencing are paving the way to a variety of revolutionary applications in modern healthcare. In particular, the increasing understanding of the human genome, and of its relation to diseases, health and to responses to treatments brings promise of improvements in better preventive and personalized medicine. Unfortunately, the impact on privacy and security is unprecedented. The genome is our ultimate identifier and, if leaked, it can unveil sensitive and personal information such as our genetic diseases, our propensity to develop certain conditions (e.g., cancer or Alzheimer's) or the health issues of our family. Even though legislation, such as the EU General Data Protection Regulation (GDPR) or the US Health Insurance Portability and Accountability Act (HIPAA), aims at mitigating abuses based on genomic and medical data, it is clear that this information also needs to be protected by technical means. In this thesis, we investigate the problem of developing new and practical privacy-enhancing technologies (PETs) for the protection of medical and genomic data. Our goal is to accelerate the adoption of PETs in the medical field in order to address the privacy and security concerns that prevent personalized medicine from reaching its full potential. We focus on two main areas of personalized medicine: clinical care and medical research. For clinical care, we first propose a system for securely storing and selectively retrieving raw genomic data that is indispensable for in-depth diagnoses and treatments of complex genetic diseases such as cancer. Then, we focus on genetic variants and devise a new model based on additively-homomorphic encryption for privacy-preserving genetic testing in clinics. Our model, implemented in the context of HIV treatment, is the first to be tested and evaluated by practitioners in a real operational setting. For medical research, we first propose a method that combines somewhat-homomorphic encryption with differential privacy to enable secure feasibility studies on genetic data stored at an untrusted central repository. Second, we address the problem of sharing genomic and medical data when the data is distributed across multiple mistrustful institutions. We begin by analyzing the risks that threaten patientsâ privacy in systems for the discovery of genetic variants, and we propose practical mitigations to the re-identification risk. Then, for clinical sites to be able to share the data without worrying about the risk of data breaches, we develop a new system based on collective homomorphic encryption: it achieves trust decentralization and enables researchers to securely find eligible patients for clinical studies. Finally, we design a new framework, complementary to the previous ones, for quantifying the risk of unintended disclosure caused by potential inference attacks that are jointly combined by a malicious adversary, when exact genomic data is shared. In summary, in this thesis we demonstrate that PETs, still believed unpractical and immature, can be made practical and can become real enablers for overcoming the privacy and security concerns blocking the advancement of personalized medicine. Addressing privacy issues in healthcare remains a great challenge that will increasingly require long-term collaboration among geneticists, healthcare providers, ethicists, lawmakers, and computer scientists.

EPFL2018

Computational analysis of ultraconserved non-coding DNA elements in vertebrate genomes

Slavica Dimitrieva Janeva

Recent advancements in DNA sequencing technologies, accompanied by parallel development of sophisticated computational methods, offer an unprecedented opportunity to explore and study the genetic material of many organisms. However, despite the tremendous progress in the field of genomics, our understanding of the genetic information encoded by the DNA is far from complete. Whole-genome comparisons of vertebrate species have uncovered the existence of non-coding DNA sequences that exhibit exceptionally high levels of similarity over hundreds of base pairs across distant species, referred to as ultraconserved non-coding elements (UCNEs). The existence of such sequences represents one of the biggest mysteries in current biology. Many UCNEs exhibit even stronger conservation than protein coding regions, but to date no molecular mechanism has been described that would require such a high degree of conservation over such long sequences. This thesis focuses on computational analysis of UCNEs across vertebrate genomes, with an ultimate goal to provide insights into the functioning of these elements and to unravel the reasons for their extreme conservation. Using computational approaches we studied the salient characteristics of UCNEs, and explored their genomic environment and their organization across genomes. Like previous studies, we observed that UCNEs are organized as large clusters around essential developmental genes, forming genomic regulatory blocks. Then we sought to understand the reasons behind this strong clustering of UCNEs. The clustering could reflect functional cooperativity between UCNEs. Alternatively, if each UCNE acts independently, their high concentration near developmental genes could merely reflect the extreme regulatory complexity of these genes. In a special setting of genomic context analysis, we analyzed the fate of UCNEs in teleost fish genomes that were subjected to an additional round of whole-genome duplication. We found that in most cases all UCNEs of a block were retained in one copy only, but together on the same chromosome. Conversely, the corresponding target genes were often retained in two copies, one completely devoid of UCNEs. Our results suggested that UCNEs of a cluster function in a highly cooperative manner. We propose that a multitude of cooperative cis-interactions between UCNEs is the reason for their extreme sequence conservation. Our work presents a novel hypothesis about the reasons for UCNE conservation and their mode of action, which is yet to be confirmed experimentally. The results from our study are made accessible through a new web resource called UCNEbase (http://ccg.vital-it.ch/UCNEbase). UCNEbase provides informa- tion about the genomic organization of UCNEs and their association with developmental regulatory genes, and enables all data to be explored in a genome browser environment. As part of a side project, we developed a new sparsified version of algorithms for computational prediction of RNA secondary structures and introduced a new program sibRNAfold. We performed a thorough analysis of the time complexity of sparsified RNA folding algorithms covering a large parameter space and empirically demonstrated that sparse RNA folding has cubic time complexity rather than quadratic, as claimed previously.

EPFL2013

Genomic targets and molecular partners of the chromatin regulator KAP1

Annamaria Kauzlaric

KAP1 is an enigmatic regulatory protein, first described some twenty years ago, shown to be involved in multiple and diverse cellular functions. Specifically, it mediates tasks critical to cell growth and differentiation, pluripotency, apoptosis, gene silencing, DNA repair and genomic integrity maintenance. In line with these findings, Kap1 deletion results in embryonic lethality, and its conditional ablation impairs crucial processes such as erythropoiesis, hepatic metabolism homeostasis and stress response. Initially named also TIF1ðœ, transcriptional intermediary factor 1ðœ, KAP1 was thought to mediate mostly chromatin-related functions. Accordingly, global maps of genomic recruitment of KAP1, generated with the advent of high-throughput technologies, display binding of this protein to thousands of loci in a multiplicity of cell types. Coupling such maps with data of transcriptional perturbations detected upon Kap1 depletion, and with the protein complexes associated with KAP1, proved to be a highly efficient approach to elucidate the molecular functions of KAP1. In particular, such studies unveiled the mechanisms of KAP1-mediated repression of different classes of transposable elements (TEs), and led to important evolutionary considerations on the strategies evolved by higher organisms to restrict the activity of these entities. Through its multiple functional domains, KAP1 interacts with a broad range of proteins, and our first goal was to establish the complete set of such potential cofactors. We found marked enrichment of KAP1 in the chromatin fraction of the nucleus, and accordingly, among its associated proteins, a strong preponderance of nucleic-acid contacting proteins. We revealed that KAP1 is part of a variety of relatively low molecular weight complexes, co-existing at comparable abundances. Study of the molecular functions of the hits suggested that KAP1 could partake in processes thus far unexplored in this context, such as DNA replication and active transcription. We then pursued with the thorough characterization of genome-wide KAP1 binding sites, and found significant fractions of RNA polymerase II (PolII)-dependent promoters and RNA polymerase III (PolIII)-transcribed elements, for the majority enriched with euchromatic marks, to be targeted by KAP1. Using high-throughput techniques, we uncovered that KAP1 variably affected the PolII pausing index of defined sets of genes, and downregulated transcription of tRNA genes. Our analyses of KAP1 genomic recruitments further led us to the discovery of novel genetic entities in the mouse genome, highly related to KRAB zinc finger protein (KZFP) genes. We described possible mechanisms underlying the evolution and amplification of these sequences and more broadly of the KZFP genes family. Furthermore, our data supported the existence of a KAP1-dependent regulatory circuit taming TE-contained enhancers located in the vicinity of these genetic units, thereby regulating their expression. We explored the proposed models of KAP1 functions in euchromatic regions of the genome, and with our results we provided the most complete analysis of the interactome of KAP1 thus far reported. Moreover, we annotated new potentially protein-coding transcripts belonging to the biggest single family of transcription factors encoded in the murine and human genomes. In sum, this work brings novel pieces of information that complement the current knowledge in the field, opening the way to further molecular studies clarifying the physiological roles of KAP1 in higher vertebrates.

EPFL2017