Exploiting the pathway structure of metabolism to reveal high-order epistasis

<p>Abstract</p> <p>Background</p> <p>Biological robustness results from redundant pathways that achieve an essential objective, e.g. the production of biomass. As a consequence, the biological roles of many genes can only be revealed through multiple knockouts that identify a <it>set </it>of genes as essential for a given function. The identification of such "epistatic" essential relationships between network components is critical for the understanding and eventual manipulation of robust systems-level phenotypes.</p> <p>Results</p> <p>We introduce and apply a network-based approach for genome-scale metabolic knockout design. We apply this method to uncover over 11,000 minimal knockouts for biomass production in an <it>in silico </it>genome-scale model of <it>E. coli</it>. A large majority of these "essential sets" contain 5 or more reactions, and thus represent complex epistatic relationships between components of the <it>E. coli </it>metabolic network.</p> <p>Conclusion</p> <p>The complex minimal biomass knockouts discovered with our approach illuminate robust essential systems-level roles for reactions in the <it>E. coli </it>metabolic network. Unlike previous approaches, our method yields results regarding high-order epistatic relationships and is applicable at the genome-scale.</p

Synthetic approaches to understanding biological constraints

Microbes can be readily cultured and their genomes can be easily manipulated. For these reasons, laboratory systems of unicellular organisms are increasingly used to develop and test theories about biological constraints, which manifest themselves at different levels of biological organization, from optimal gene-expression levels to complex individual and social behaviors. The quantitative description of biological constraints has recently advanced in several areas, such as the formulation of global laws governing the entire economy of a cell, the direct experimental measurement of the trade-offs leading to optimal gene expression, the description of naturally occurring fitness landscapes, and the appreciation of the requirements for a stable bacterial ecosystem.Alfred P. Sloan Foundation (Fellowship)Pew Charitable Trusts (Pew Scholars Program)National Science Foundation (U.S.) (NSF CAREER Award)National Institutes of Health (U.S.) (NIH R00 Pathway to Independence Award

Mapping genetic interactions in cancer: a road to rational combination therapies.

The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies

A General Model for Multilocus Epistatic Interactions in Case-Control Studies

Background: Epistasis, i.e., the interaction of alleles at different loci, is thought to play a central role in the formation and progression of complex diseases. The complexity of disease expression should arise from a complex network of epistatic interactions involving multiple genes. Methodology: We develop a general model for testing high-order epistatic interactions for a complex disease in a casecontrol study. We incorporate the quantitative genetic theory of high-order epistasis into the setting of cases and controls sampled from a natural population. The new model allows the identification and testing of epistasis and its various genetic components. Conclusions: Simulation studies were used to examine the power and false positive rates of the model under different sampling strategies. The model was used to detect epistasis in a case-control study of inflammatory bowel disease, in which five SNPs at a candidate gene were typed, leading to the identification of a significant three-locus epistasis

Bioinformatics challenges for genome-wide association studies

Motivation: The sequencing of the human genome has made it possible to identify an informative set of >1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving health care through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype–phenotype relationship that is characterized by significant heterogeneity and gene–gene and gene–environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods

Functional and evolutionary implications of in silico gene deletions

Understanding how genetic modifications, individual or in combination, affect organismal fitness or other phenotypes is a challenge common to several areas of biology, including human health & genetics, metabolic engineering, and evolutionary biology. The importance of a gene can be quantified by measuring the phenotypic impact of its associated genetic perturbations "here and now", e.g. the growth rate of a mutant microbe. However, each gene also maintains a historical record of its cumulative importance maintained throughout millions of years of natural selection in the form of its degree of sequence conservation along phylogenetic branches. This thesis focuses on whether and how the phenotypic and evolutionary importance of genes are related to each other. Towards this goal, I developed a new approach for characterizing the phenotypic consequences of genetic modifications in genome-scale biochemical networks using constraint-based computational models of metabolism. In particular, I investigated the impact of gene loss events on fitness in the model organism Saccharomyces cerevisiae, and found that my new metric for estimating the cost of gene deletion correlates with gene evolutionary rate. I found that previous failures to uncover this correlation using similar techniques may have been the result of an incorrect assumption about how isoenzymes deletions affect the reaction they catalyze. I next hypothesized that the improvement my metric showed in predicting the cost of isoenzyme loss could translate into an improved capacity to predict the impact of pairs of gene deletions involving isoenzymes. Studies of such pair-wise genetic perturbations are important, because the extent to which a genetic perturbation modifies any given phenotype is often dependent on the genetic background upon which it has been performed. This lack of independence within sets of perturbations is termed epistasis. My results showed that, indeed, the new metric displays an increased capacity to predict epistatic interactions between pairs of genes. In addition to shedding light on the relationship between the functional and evolutionary importance of genes, further developments of our approach may lead to better prediction of gene knockout phenotypes, with applications ranging from metabolic engineering to the search for gene targets for therapeutic applications

Controlling the Outcome of the Toll-Like Receptor Signaling Pathways

The Toll-Like Receptors (TLRs) are proteins involved in the immune system that increase cytokine levels when triggered. While cytokines coordinate the response to infection, they appear to be detrimental to the host when reaching too high levels. Several studies have shown that the deletion of specific TLRs was beneficial for the host, as cytokine levels were decreased consequently. It is not clear, however, how targeting other components of the TLR pathways can improve the responses to infections. We applied the concept of Minimal Cut Sets (MCS) to the ihsTLR v1.0 model of the TLR pathways to determine sets of reactions whose knockouts disrupt these pathways. We decomposed the TLR network into 34 modules and determined signatures for each MCS, i.e. the list of targeted modules. We uncovered 2,669 MCS organized in 68 signatures. Very few MCS targeted directly the TLRs, indicating that they may not be efficient targets for controlling these pathways. We mapped the species of the TLR network to genes in human and mouse, and determined more than 10,000 Essential Gene Sets (EGS). Each EGS provides genes whose deletion suppresses the network's outputs

Molecular mechanisms of drug resistance in natural Leishmania populations vary with genetic background

The evolution of drug-resistance in pathogens is a major global health threat. Elucidating the molecular basis of pathogen drug-resistance has been the focus of many studies but rarely is it known whether a drug-resistance mechanism identified is universal for the studied pathogen; it has seldom been clarified whether drug-resistance mechanisms vary with the pathogen's genotype. Nevertheless this is of critical importance in gaining an understanding of the complexity of this global threat and in underpinning epidemiological surveillance of pathogen drug resistance in the field. This study aimed to assess the molecular and phenotypic heterogeneity that emerges in natural parasite populations under drug treatment pressure. We studied lines of the protozoan parasite Leishmania (L.) donovani with differential susceptibility to antimonial drugs; the lines being derived from clinical isolates belonging to two distinct genetic populations that circulate in the leishmaniasis endemic region of Nepal. Parasite pathways known to be affected by antimonial drugs were characterised on five experimental levels in the lines of the two populations. Characterisation of DNA sequence, gene expression, protein expression and thiol levels revealed a number of molecular features that mark antimonial-resistant parasites in only one of the two populations studied. A final series of in vitro stress phenotyping experiments confirmed this heterogeneity amongst drug-resistant parasites from the two populations. These data provide evidence that the molecular changes associated with antimonial-resistance in natural Leishmania populations depend on the genetic background of the Leishmania population, which has resulted in a divergent set of resistance markers in the Leishmania populations. This heterogeneity of parasite adaptations provides severe challenges for the control of drug resistance in the field and the design of molecular surveillance tools for widespread applicability

High-order combination effects and biological robustness

Biological systems are robust, in that they can maintain stable phenotypes under varying conditions or attacks. Biological systems are also complex, being organized into many functional modules that communicate through interlocking pathways and feedback mechanisms. In these systems, robustness and complexity are linked because both qualities arise from the same underlying mechanisms. When perturbed by multiple attacks, such complex systems become fragile in both theoretical and experimental studies, and this fragility depends on the number of agents applied. We explore how this relationship can be used to study the functional robustness of a biological system using systematic high-order combination experiments. This presents a promising approach toward many biomedical and bioengineering challenges. For example, high-order experiments could determine the point of fragility for pathogenic bacteria and might help identify optimal treatments against multi-drug resistance. Such studies would also reinforce the growing appreciation that biological systems are best manipulated not by targeting a single protein, but by modulating the set of many nodes that can selectively control a system's functional state