Comprehensive interrogation of gene lists from genome-scale cancer screens with oncoEnrichR

Genome-scale screening experiments in cancer produce long lists of candidate genes that require extensive interpretation for biological insight and prioritization for follow-up studies. Interrogation of gene lists frequently represents a significant and time-consuming undertaking, in which experimental biologists typically combine results from a variety of bioinformatics resources in an attempt to portray and understand cancer relevance. As a means to simplify and strengthen the support for this endeavor, we have developed oncoEnrichR, a flexible bioinformatics tool that allows cancer researchers to comprehensively interrogate a given gene list along multiple facets of cancer relevance. oncoEnrichR differs from general gene set analysis frameworks through the integration of an extensive set of prior knowledge specifically relevant for cancer, including ranked gene-tumor type associations, literature-supported proto-oncogene and tumor suppressor gene annotations, target druggability data, regulatory interactions, synthetic lethality predictions, as well as prognostic associations, gene aberrations and co-expression patterns across tumor types. The software produces a structured and user-friendly analysis report as its main output, where versions of all underlying data resources are explicitly logged, the latter being a critical component for reproducible science. We demonstrate the usefulness of oncoEnrichR through interrogation of two candidate lists from proteomic and CRISPR screens. oncoEnrichR is freely available as a web-based service hosted by the Galaxy platform ( https://oncotools.elixir.no ), and can also be accessed as a stand-alone R package ( https://github.com/sigven/oncoEnrichR ).     

Genome-scale metabolic network reconstruction of model animals as a platform for translational research

Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer’s disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications.

Animal models have long been utilized as a fundamental tool for translational research in recapitulating phenotypic syndromes, clarifying underlying mechanisms, and translating biomedical discoveries into effective clinical treatments for human disease (1). Small rodents, including mouse (Mus musculus) and rat (Rattus norvegicus), account for 90% of the tens of millions of animals used annually in medical research (2, 3), and transgenic mice in particular are the most commonly used models for a plethora of human diseases including cancers, neurodegenerative dementia, diabetes, and many other metabolic disorders (4). In addition, with their unique anatomical and physiological features transgenic zebrafish (Danio rerio) and invertebrate models, such as the fruit fly (Drosophila melanogaster) and Nematoda worm (Caenorhabditis elegans), have been used for many years as inexpensive alternatives for studying human diseases through genetic manipulation of ortholog genes (5, 6). It is also important to note that nearly all the fundamental aspects of biology have been derived from the study of model organisms (7).A genome-scale metabolic model (GEM) is a mathematical representation of the metabolism for an organism and it provides extensive gene–reaction–metabolite connectivity via two matrices: the S matrix for associating metabolites to reactions and the rxnGeneMat matrix associating reactions to corresponding enzymes and genes (8). Given that many human diseases, including cancer, type II diabetes, and many liver- and pancreas-related diseases can be attributed to metabolic disorders (9), human GEMs have been used to describe the metabolic conditions of specific tissues and cell types at the systems level with the integration of omics data (10–13). For the purpose of clarity, henceforth “GEM” is used here to refer to a computational metabolic model, and “model” refers to a transgenic animal developed for studying human disease.The use of animal models together with GEMs poses an attractive approach to studying human disease. For example, mouse GEMs have previously been applied to investigate the influences of gut microbiota on host metabolism (14). To date, there have been a few existing GEMs for model animals, including MMR (14) and iMM1865 (15) for mouse, iRno (16) for rat, ZebraGEM (17, 18) for zebrafish, and iCEL1273 and iCEL1314 for worm (19, 20). However, these GEMs have not been developed and publicly curated to the same extent as that of yeast (21) and human (11). Their limited coverage in metabolic pathways and incompatible nomenclatures impede cross-species validation of biological discoveries and translational applications from animal models to human patients.Recently, an open and version-controlled workflow has been introduced during the development of the most comprehensive yeast and human GEM, Yeast8 (21) and Human1 (11), respectively, which present high-quality templates to develop new GEMs in a systematic and reproducible manner. Databases such as MGD (22), FlyBase (23), ZFIN (24), and WormBase (25) that provide organism-specific annotation and human orthologs have been recently integrated into a centralized portal, the Alliance of Genome Resources (26), for consistent annotation and curation of gene ontology in relation to the human counterparts. Through channeling these reliable data sources we here present a unified GEM platform for mouse, rat, zebrafish, fruit fly, and worm. The derived GEMs (Mouse1, Rat1, Zebrafish1, Fruitfly1, and Worm1, respectively) were reconstructed from a robust modeling pipeline that combines both the orthology-based metabolic network and species-specific pathways. To validate this approach, we conducted an extensive GEM comparison and gene essentiality analysis using available experimental data and demonstrated that our GEMs generally outperform the previous ones. We also showcased the usefulness of Mouse1 in systems medicine discovery by performing integrative analysis of omics data from mouse models of Alzheimer’s disease (AD). We are confident that this versatile GEM platform covering all major model animals will greatly enhance the utilization of omics data from disease models in facilitating translational studies.     

酵母基因组尺度遗传相互作用网络的拓扑性质

目的分析酵母遗传相互作用的基因组尺度的总体性质。方法用网络的图论方法,计算酵母遗传相互作用网络的拓扑性质。结果酵母全基因组的遗传相互作用中,基因的相互作用个数服从幂律分布,幂指数接近3。平均相互作用的基因为87,约2/3的基因只需通过1个基因就有遗传相互作用,约1/3只需通过2个基因就有遗传相互作用。遗传相互作用网络的平均聚集系数是0.047。结论酵母的基因通常具有多种而不是单一的功能,而且基因之间遗传相互作用高度密集。     

Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses

Flux balance analysis (FBA) of a genome-scale metabolic model allows calculation of intracellular fluxes by optimizing an objective function, such as maximization of cell growth, under given constraints, and has found numerous applications in the field of systems biology and biotechnology. Due to the underdetermined nature of the system, however, it has limitations such as inaccurate prediction of fluxes and existence of multiple solutions for an optimal objective value. Here, we report a strategy for accurate prediction of metabolic fluxes by FBA combined with systematic and condition-independent constraints that restrict the achievable flux ranges of grouped reactions by genomic context and flux-converging pattern analyses. Analyses of three types of genomic contexts, conserved genomic neighborhood, gene fusion events, and co-occurrence of genes across multiple organisms, were performed to suggest a group of fluxes that are likely on or off simultaneously. The flux ranges of these grouped reactions were constrained by flux-converging pattern analysis. FBA of the Escherichia coli genome-scale metabolic model was carried out under several different genotypic (pykF, zwf, ppc, and sucA mutants) and environmental (altered carbon source) conditions by applying these constraints, which resulted in flux values that were in good agreement with the experimentally measured ¹³C-based fluxes. Thus, this strategy will be useful for accurately predicting the intracellular fluxes of large metabolic networks when their experimental determination is difficult.