Genotype-Environment Interactions Reveal Causal Pathways That Mediate Genetic Effects on Phenotype

Unraveling the molecular processes that lead from genotype to phenotype is crucial for the understanding and effective treatment of genetic diseases. Knowledge of the causative genetic defect most often does not enable treatment; therefore, causal intermediates between genotype and phenotype constitute valuable candidates for molecular intervention points that can be therapeutically targeted. Mapping genetic determinants of gene expression levels (also known as expression quantitative trait loci or eQTL studies) is frequently used for this purpose, yet distinguishing causation from correlation remains a significant challenge. Here, we address this challenge using extensive, multi-environment gene expression and fitness profiling of hundreds of genetically diverse yeast strains, in order to identify truly causal intermediate genes that condition fitness in a given environment. Using functional genomics assays, we show that the predictive power of eQTL studies for inferring causal intermediate genes is poor unless performed across multiple environments. Surprisingly, although the effects of genotype on fitness depended strongly on environment, causal intermediates could be most reliably predicted from genetic effects on expression present in all environments. Our results indicate a mechanism explaining this apparent paradox, whereby immediate molecular consequences of genetic variation are shared across environments, and environment-dependent phenotypic effects result from downstream integration of environmental signals. We developed a statistical model to predict causal intermediates that leverages this insight, yielding over 400 transcripts, for the majority of which we experimentally validated their role in conditioning fitness. Our findings have implications for the design and analysis of clinical omics studies aimed at discovering personalized targets for molecular intervention, suggesting that inferring causation in a single cellular context can benefit from molecular profiling in multiple contexts

Metabolic design of macroscopic bioreaction models: application to Chinese hamster ovary cells

The aim of this paper is to present a systematic methodology to design macroscopic bioreaction models for cell cultures based upon metabolic networks. The cell culture is seen as a succession of phases. During each phase, a metabolic network represents the set of reactions occurring in the cell. Then, through the use of the elementary flux modes, these metabolic networks are used to derive macroscopic bioreactions linking the extracellular substrates and products. On this basis, as many separate models are obtained as there are phases. Then, a complete model is obtained by smoothly switching from model to model. This is illustrated with batch cultures of Chinese hamster ovary cells

Complex networks theory for analyzing metabolic networks

One of the main tasks of post-genomic informatics is to systematically investigate all molecules and their interactions within a living cell so as to understand how these molecules and the interactions between them relate to the function of the organism, while networks are appropriate abstract description of all kinds of interactions. In the past few years, great achievement has been made in developing theory of complex networks for revealing the organizing principles that govern the formation and evolution of various complex biological, technological and social networks. This paper reviews the accomplishments in constructing genome-based metabolic networks and describes how the theory of complex networks is applied to analyze metabolic networks.Comment: 13 pages, 2 figure

The response of perennial and temporary headwater stream invertebrate communities to hydrological extremes

The headwaters of karst rivers experience considerable hydrological variability, including spates and streambed drying. Extreme summer flooding on the River Lathkill (Derbyshire, UK) provided the opportunity to examine the invertebrate community response to unseasonal spate flows, flow recession and, at temporary sites, streambed drying. Invertebrates were sampled at sites with differing flow permanence regimes during and after the spates. Following streambed drying at temporary sites, dewatered surface sediments were investigated as a refugium for aquatic invertebrates. Experimental rehydration of these dewatered sediments was conducted to promote development of desiccation-tolerant life stages. At perennial sites, spate flows reduced invertebrate abundance and diversity, whilst at temporary sites, flow reactivation facilitated rapid colonisation of the surface channel by a limited number of invertebrate taxa. Following streambed drying, 38 taxa were recorded from the dewatered and rehydrated sediments, with Oligochaeta being the most abundant taxon and Chironomidae (Diptera) the most diverse. Experimental rehydration of dewatered sediments revealed the presence of additional taxa, including Stenophylax sp. (Trichoptera: Limnephilidae) and Nemoura sp. (Plecoptera: Nemouridae). The influence of flow permanence on invertebrate community composition was apparent despite the aseasonal high-magnitude flood events

Technical design of the phase I Mu3e experiment

The Mu3e experiment aims to find or exclude the lepton flavour violating decay at branching fractions above . A first phase of the experiment using an existing beamline at the Paul Scherrer Institute (PSI) is designed to reach a single event sensitivity of . We present an overview of all aspects of the technical design and expected performance of the phase I Mu3e detector. The high rate of up to muon decays per second and the low momenta of the decay electrons and positrons pose a unique set of challenges, which we tackle using an ultra thin tracking detector based on high-voltage monolithic active pixel sensors combined with scintillating fibres and tiles for precise timing measurements

An instrument to measure psychosocial determinants of health care professionals' vaccination behavior: Validation of the Pro-VC-Be questionnaire

Objectives: The lack of validated instruments assessing vaccine hesitancy/confidence among health care professionals (HCPs) for themselves, and their patients led us to develop and validate the Pro-VC-Be instrument to measure vaccine confidence and other psychosocial determinants of HCPs' vaccination behavior among diverse HCPs in different countries.Methods: Cross-sectional survey in October-November 2020 among 1,249 GPs in France, 432 GPs in French-speaking parts of Belgium, and 1,055 nurses in Quebec (Canada), all participating in general population immunization. Exploratory and confirmatory factor analyses evaluated the instrument's construct validity. We used HCPs' self-reported vaccine recommendations to patients, general immunization activity, self-vaccination, and future COVID-19 vaccine acceptance to test criterion validity.Results: The final results indicated a 6-factor structure with good fit: vaccine confidence (combining complacency, perceived vaccine risks, perceived benefit-risk balance, perceived collective responsibility), trust in authorities, perceived constraints, proactive efficacy (combining commitment to vaccination and self-efficacy), reluctant trust, and openness to patients. The instrument showed good convergent and criterion validity and adequate discriminant validity.Conclusions: This study found that the Pro-VC-Be is a valid instrument for measuring psychosocial determinants of HCPs' vaccination behaviors in different settings. Its validation is currently underway in Europe among various HCPs in different languages.</p

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

A systems approach to identifying correlated gene targets for the loss of colour pigmentation in plants

<p>Abstract</p> <p>Background</p> <p>The numerous diverse metabolic pathways by which plant compounds can be produced make it difficult to predict how colour pigmentation is lost for different tissues and plants. This study employs mathematical and <it>in silico </it>methods to identify correlated gene targets for the loss of colour pigmentation in plants from a whole cell perspective based on the full metabolic network of <it>Arabidopsis</it>. This involves extracting a self-contained flavonoid subnetwork from the AraCyc database and calculating feasible metabolic routes or elementary modes (EMs) for it. Those EMs leading to anthocyanin compounds are taken to constitute the anthocyanin biosynthetic pathway (ABP) and their interplay with the rest of the EMs is used to study the minimal cut sets (MCSs), which are different combinations of reactions to block for eliminating colour pigmentation. By relating the reactions to their corresponding genes, the MCSs are used to explore the phenotypic roles of the ABP genes, their relevance to the ABP and the impact their eliminations would have on other processes in the cell.</p> <p>Results</p> <p>Simulation and prediction results of the effect of different MCSs for eliminating colour pigmentation correspond with existing experimental observations. Two examples are: i) two MCSs which require the simultaneous suppression of genes DFR and ANS to eliminate colour pigmentation, correspond to observational results of the same genes being co-regulated for eliminating floral pigmentation in <it>Aquilegia </it>and; ii) the impact of another MCS requiring CHS suppression, corresponds to findings where the suppression of the early gene CHS eliminated nearly all flavonoids but did not affect the production of volatile benzenoids responsible for floral scent.</p> <p>Conclusions</p> <p>From the various MCSs identified for eliminating colour pigmentation, several correlate to existing experimental observations, indicating that different MCSs are suitable for different plants, different cells, and different conditions and could also be related to regulatory genes. Being able to correlate the predictions with experimental results gives credence to the use of these mathematical and <it>in silico </it>analyses methods in the design of experiments. The methods could be used to prioritize target enzymes for different objectives to achieve desired outcomes, especially for less understood pathways.</p

A new computational method to split large biochemical networks into coherent subnets

<p>Abstract</p> <p>Background</p> <p>Compared to more general networks, biochemical networks have some special features: while generally sparse, there are a small number of highly connected metabolite nodes; and metabolite nodes can also be divided into two classes: internal nodes with associated mass balance constraints and external ones without. Based on these features, reclassifying selected internal nodes (separators) to external ones can be used to divide a large complex metabolic network into simpler subnetworks. Selection of separators based on node connectivity is commonly used but affords little detailed control and tends to produce excessive fragmentation.</p> <p>The method proposed here (Netsplitter) allows the user to control separator selection. It combines local connection degree partitioning with global connectivity derived from random walks on the network, to produce a more even distribution of subnetwork sizes. Partitioning is performed progressively and the interactive visual matrix presentation used allows the user considerable control over the process, while incorporating special strategies to maintain the network integrity and minimise the information loss due to partitioning.</p> <p>Results</p> <p>Partitioning of a genome scale network of 1348 metabolites and 1468 reactions for <it>Arabidopsis thaliana </it>encapsulates 66% of the network into 10 medium sized subnets. Applied to the flavonoid subnetwork extracted in this way, it is shown that Netsplitter separates this naturally into four subnets with recognisable functionality, namely synthesis of lignin precursors, flavonoids, coumarin and benzenoids. A quantitative quality measure called <it>efficacy </it>is constructed and shows that the new method gives improved partitioning for several metabolic networks, including bacterial, plant and mammal species.</p> <p>Conclusions</p> <p>For the examples studied the Netsplitter method is a considerable improvement on the performance of connection degree partitioning, giving a better balance of subnet sizes with the removal of fewer mass balance constraints. In addition, the user can interactively control which metabolite nodes are selected for cutting and when to stop further partitioning as the desired granularity has been reached. Finally, the blocking transformation at the heart of the procedure provides a powerful visual display of network structure that may be useful for its exploration independent of whether partitioning is required.</p

NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways

The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources