Nathan D. Price

A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses.

Microalgae have reemerged as organisms of prime biotechnological interest due to their ability to synthesize a suite of valuable chemicals. To harness the capabilities of these organisms, we need a comprehensive systems-level understanding of their metabolism, which can be fundamentally achieved through large-scale mechanistic models of metabolism. In this study, we present a revised and significantly improved genome-scale metabolic model for the widely-studied microalga, Chlamydomonas reinhardtii. The model, iCre1355, represents a major advance over previous models, both in content and predictive power. iCre1355 encompasses a broad range of metabolic functions encoded across the nuclear, chloroplast and mitochondrial genomes accounting for 1355 genes (1460 transcripts), 2394 and 1133 metabolites. We found improved performance over the previous metabolic model based on comparisons of predictive accuracy across 306 phenotypes (from 81 mutants), lipid yield analysis and growth rates derived from chemostat-grown cells (under three conditions). Measurement of macronutrient uptake revealed carbon and phosphate to be good predictors of growth rate, while nitrogen consumption appeared to be in excess. We analyzed high-resolution time series transcriptomics data using iCre1355 to uncover dynamic pathway-level changes that occur in response to nitrogen starvation and changes in light intensity. This approach enabled accurate prediction of growth rates, the cessation of growth and accumulation of triacylglycerols during nitrogen starvation, and the temporal response of different growth-associated pathways to increased light intensity. Thus, iCre1355 represents an experimentally validated genome-scale reconstruction of C. reinhardtii metabolism that should serve as a useful resource for studying the metabolic processes of this and related microalgae.

AUREA: an open-source software system for accurate and user-friendly identification of relative expression molecular signatures

BackgroundPublic databases such as the NCBI Gene Expression Omnibus contain extensive and exponentially increasing amounts of high-throughput data that can be applied to molecular phenotype characterization. Collectively, these data can be analyzed for such purposes as disease diagnosis or phenotype classification. One family of algorithms that has proven useful for disease classification is based on relative expression analysis and includes the Top-Scoring Pair (TSP), k-Top-Scoring Pairs (k-TSP), Top-Scoring Triplet (TST) and Differential Rank Conservation (DIRAC) algorithms. These relative expression analysis algorithms hold significant advantages for identifying interpretable molecular signatures for disease classification, and have been implemented previously on a variety of computational platforms with varying degrees of usability. To increase the user-base and maximize the utility of these methods, we developed the program AUREA (Adaptive Unified Relative Expression Analyzer)—a cross-platform tool that has a consistent application programming interface (API), an easy-to-use graphical user interface (GUI), fast running times and automated parameter discovery.ResultsHerein, we describe AUREA, an efficient, cohesive, and user-friendly open-source software system that comprises a suite of methods for relative expression analysis. AUREA incorporates existing methods, while extending their capabilities and bringing uniformity to their interfaces. We demonstrate that combining these algorithms and adaptively tuning parameters on the training sets makes these algorithms more consistent in their performance and demonstrate the effectiveness of our adaptive parameter tuner by comparing accuracy across diverse datasets.ConclusionsWe have integrated several relative expression analysis algorithms and provided a unified interface for their implementation while making data acquisition, parameter fixing, data merging, and results analysis ‘point-and-click’ simple. The unified interface and the adaptive parameter tuning of AUREA provide an effective framework in which to investigate the massive amounts of publically available data by both ‘in silico’ and ‘bench’ scientists. AUREA can be found at http://price.systemsbiology.net/AUREA/.

SNAPR: A Bioinformatics Pipeline for Efficient and Accurate RNA-Seq Alignment and Analysis

The process of converting raw RNA sequencing (RNA-seq) data to interpretable results can be circuitous and time-consuming, requiring multiple steps. We present an RNA-seq mapping algorithm that streamlines this process. Our algorithm utilizes a hash table approach to leverage the availability and the power of high memory machines. SNAPR, which can be run on a single library or thousands of libraries, can take compressed or uncompressed FASTQ and BAM files, and output a sorted BAM file, individual read counts, and gene fusions, and can identify exogenous RNA species in a single step. SNAPR also does native Phred score filtering of reads. SNAPR is also well suited for future sequencing platforms that generate longer reads. We show how we can analyze data from hundreds of TCGA samples in a matter of hours while identifying gene fusions and viral events at the same time. With the reference genome and transcriptome undergoing periodic updates and the need for uniform parameters when integrating multiple data sets, there is great need for a streamlined process for RNA-seq analysis. We demonstrate how SNAPR does this efficiently and accurately.

Genome-wide pleiotropy analysis of neuropathological traits related to Alzheimer's disease

BackgroundSimultaneous consideration of two neuropathological traits related to Alzheimer’s disease (AD) has not been attempted in a genome-wide association study.MethodsWe conducted genome-wide pleiotropy analyses using association summary statistics from the Beecham et al. study (PLoS Genet 10:e1004606, 2014) for AD-related neuropathological traits, including neuritic plaque (NP), neurofibrillary tangle (NFT), and cerebral amyloid angiopathy (CAA). Significant findings were further examined by expression quantitative trait locus and differentially expressed gene analyses in AD vs. control brains using gene expression data.ResultsGenome-wide significant pleiotropic associations were observed for the joint model of NP and NFT (NP + NFT) with the single-nucleotide polymorphism (SNP) rs34487851 upstream of C2orf40 (alias ECRG4, P = 2.4 × 10−8) and for the joint model of NFT and CAA (NFT + CAA) with the HDAC9 SNP rs79524815 (P = 1.1 × 10−8). Gene-based testing revealed study-wide significant associations (P ≤ 2.0 × 10−6) for the NFT + CAA outcome with adjacent genes TRAPPC12, TRAPPC12-AS1, and ADI1. Risk alleles of proxy SNPs for rs79524815 were associated with significantly lower expression of HDAC9 in the brain (P = 3.0 × 10−3), and HDAC9 was significantly downregulated in subjects with AD compared with control subjects in the prefrontal (P = 7.9 × 10−3) and visual (P = 5.6 × 10−4) cortices.ConclusionsOur findings suggest that pleiotropy analysis is a useful approach to identifying novel genetic associations with complex diseases and their endophenotypes. Functional studies are needed to determine whether ECRG4 or HDAC9 is plausible as a therapeutic target.

Chapter 11 – Systems Biology and Systems Medicine

Publisher Summary A systems approach to medicine argues that disease arises from disease-perturbed biological networks and the dynamically changing, altered patterns of gene expression controlled by these perturbed networks give rise to the disease manifestations. This chapter presents a systems view of biology and disease, and recent advances in state-of-the-art in vitro and in vivo diagnostics technologies. As these technologies mature, they will move towards a future of predictive, personalized, preventive, and participatory medicine. Two primary domains of biological information lend themselves readily to systems-level analysis: the static, digital information of the genome, and the dynamic information arising from environmental interactions with the subcellular, cellular, and tissue levels of organization. Digital genome information encodes two types of biological networks–protein interactions and gene regulatory networks. Protein networks transmit biological information for development, physiology, and metabolism. Other RNAs interacting with one another receive information from signal-transduction networks, integrate and modulate it, and convey the processed information to networks of genes or molecular machines that execute developmental and physiological functions.