Personalized pathway enrichment map of putative cancer genes from next generation sequencing data

BACKGROUND: Pathway analysis of a set of genes represents an important area in large-scale omic data analysis. However, the application of traditional pathway enrichment methods to next-generation sequencing (NGS) data is prone to several potential biases, including genomic/genetic factors (e.g., the particular disease and gene length) and environmental factors (e.g., personal life-style and frequency and dosage of exposure to mutagens). Therefore, novel methods are urgently needed for these new data types, especially for individual-specific genome data. METHODOLOGY: In this study, we proposed a novel method for the pathway analysis of NGS mutation data by explicitly taking into account the gene-wise mutation rate. We estimated the gene-wise mutation rate based on the individual-specific background mutation rate along with the gene length. Taking the mutation rate as a weight for each gene, our weighted resampling strategy builds the null distribution for each pathway while matching the gene length patterns. The empirical P value obtained then provides an adjusted statistical evaluation. PRINCIPAL FINDINGS/CONCLUSIONS: We demonstrated our weighted resampling method to a lung adenocarcinomas dataset and a glioblastoma dataset, and compared it to other widely applied methods. By explicitly adjusting gene-length, the weighted resampling method performs as well as the standard methods for significant pathways with strong evidence. Importantly, our method could effectively reject many marginally significant pathways detected by standard methods, including several long-gene-based, cancer-unrelated pathways. We further demonstrated that by reducing such biases, pathway crosstalk for each individual and pathway co-mutation map across multiple individuals can be objectively explored and evaluated. This method performs pathway analysis in a sample-centered fashion, and provides an alternative way for accurate analysis of cancer-personalized genomes. It can be extended to other types of genomic data (genotyping and methylation) that have similar bias problems.     

Metabolic pathway analysis: basic concepts and scientific applications in the post-genomic era.

This article reviews the relatively short history of metabolic pathway analysis. Computer-aided algorithms for the synthesis of metabolic pathways are discussed. Important algebraic concepts used in pathway analysis, such as null space and convex cone, are explained. It is demonstrated how these concepts can be translated into meaningful metabolic concepts. For example, it is shown that the simplest vectors spanning the region of all admissible fluxes in stationary states, for which the term elementary flux modes was coined, correspond to fundamental pathways in the system. The concepts are illustrated with the help of a reaction scheme representing the glyoxylate cycle and adjacent reactions of aspartate and glutamate synthesis. The interrelations between pathway analysis and metabolic control theory are outlined. Promising applications for genome annotation and for biotechnological purposes are discussed. Armed with a better understanding of the architecture of cellular metabolism and the enormous amount of genomic data available today, biochemists and biotechnologists will be able to draw the entire metabolic map of a cell and redesign it by rational and directed metabolic engineering.     

The puzzle of the Krebs citric acid cycle: Assembling the pieces of chemically feasible reactions,and opportunism in the design of metabolic pathways during evolution

The evolutionary origin of the Krebs citric acid cycle has been for a long time a model case in the understanding of the origin and evolution of metabolic pathways: How can the emergence of such a complex pathway be explained? A number of speculative studies have been carried out that have reached the conclusion that the Krebs cycle evolved from pathways for amino acid biosynthesis, but many important questions remain open: Why and how did the full pathway emerge from there? Are other alternative routes for the same purpose possible? Are they better or worse? Have they had any opportunity to be developed in cellular metabolism evolution? We have analyzed the Krebs cycle as a problem of chemical design to oxidize acetate yielding reduction equivalents to the respiratory chain to make ATP. Our analysis demonstrates that although there are several different chemical solutions to this problem, the design of this metabolic pathway as it occurs in living cells is the best chemical solution: It has the least possible number of steps and it also has the greatest ATP yielding. Study of the evolutionary possibilities of each one-taking the available material to build new pathways-demonstrates that the emergence of the Krebs cycle has been a typical case of opportunism in molecular evolution. Our analysis proves, therefore, that the role of opportunism in evolution has converted a problem of several possible chemical solutions into asingle-solution problem, with the actual Krebs cycle demonstrated to be the best possible chemical design. Our results also allow us to derive the rules under which metabolic pathways emerged during the origin of life.     

Evaluation of computational metabolic-pathway predictions for Helicobacter pylori

MOTIVATION: We seek to determine the accuracy of computational methods for predicting metabolic pathways in sequenced genomes, and to understand the contributions of both the prediction algorithms, and the reference pathway databases used by those algorithms, to the prediction accuracy. RESULTS: The comparisons we performed were as follows. (1) We compared two predictions of the pathway complements of Helicobacter pylori that were computed by an early version of our pathway-prediction algorithm: prediction A used the EcoCyc E. coli pathway DB as the reference database (DB) for prediction, and prediction B used the MetaCyc pathway DB (a superset of EcoCyc) as the reference pathway DB. The MetaCyc-based prediction contained 75% more pathway predictions, but we believe a significant number of those predictions were false positives. (2) We compared two predictions of the pathway complement of H. pylori that used MetaCyc as the reference pathway DB, but that used different algorithms: the original PathoLogic algorithm, and an enhanced version of the algorithm designed to eliminate false-positive pathway predictions. The improved algorithm predicted 30\% fewer metabolic pathways than the original algorithm; all of the eliminated pathways are believed to be false-positive predictions. (3) We compared the 98 pathways predicted by the enhanced algorithm with the results of a manual analysis of the pathways of H. pylori. Results: 40 of the computationally predicted pathways were consistent with the manual analysis, 13 pathways are considered false-positive predictions, and four pathways had partially overlapping topologies. Twenty-six predicted pathways were not mentioned in the manual analysis; we believe these are correct predictions by PathoLogic that were not found by the manual analysis. Five pathways from the manual analysis were not found computationally. Agreement between the computational and manual predictions was good overall, with the computational analysis inferring many pathways that the manual analysis did not identify. Ultimately the manual analysis is also partially speculative, and therefore is not an absolute measure of correctness. The algorithm is designed to err on the side of more false positives to bring more potential pathways to the user's attention. The resulting H. pylori pathway DB is freely available at http://ecocyc.org:1555/HPY/organism-summary?object=HPY. AVAILABILITY: The Pathway Tools software is freely available to academic users, and is available to commercial users for a fee. Contact pkarp@ai.sri.com for information on obtaining the software.     

Pathway Miner: extracting gene association networks from molecular pathways for predicting the biological significance of gene expression microarray data

We have developed a web-based system (Pathway Miner) for visualizing gene expression profiles in the context of biological pathways. Pathway Miner catalogs genes based on their role in metabolic, cellular and regulatory pathways. A Fisher exact test is provided as an option to rank pathways. The genes are mapped onto pathways and gene product association networks are extracted for genes that co-occur in pathways. The networks can be filtered for analysis based on user-selected options. AVAILABILITY: Pathway Miner is a freely available web accessible tool at http://www.biorag.org/pathway.html     

Tools and strategies for constructing cell-free enzyme pathways

Single enzyme systems or engineered microbial hosts have been used for decades but the notion of assembling multiple enzymes into cell-free synthetic pathways is a relatively new development. The extensive possibilities that stem from this synthetic concept makes it a fast growing and potentially high impact field for biomanufacturing fine and platform chemicals, pharmaceuticals and biofuels. However, the translation of individual single enzymatic reactions into cell-free multi-enzyme pathways is not trivial. In reality, the kinetics of an enzyme pathway can be very inadequate and the production of multiple enzymes can impose a great burden on the economics of the process. We examine here strategies for designing synthetic pathways and draw attention to the requirements of substrates, enzymes and cofactor regeneration systems for improving the effectiveness and sustainability of cell-free biocatalysis. In addition, we comment on methods for the immobilisation of members of a multi-enzyme pathway to enhance the viability of the system. Finally, we focus on the recent development of integrative tools such as in silico pathway modelling and high throughput flux analysis with the aim of reinforcing their indispensable role in the future of cell-free biocatalytic pathways for biomanufacturing.     

Exploring biochemical pathways for mono-ethylene glycol (MEG) synthesis from synthesis gas

Mono-ethylene glycol (MEG) is an important petrochemical with widespread use in numerous consumer products. The current industrial MEG-production process relies on non-renewable fossil fuel-based feedstocks, such as petroleum, natural gas, and naphtha; hence, it is useful to explore alternative routes of MEG-synthesis from gases as they might provide a greener and more sustainable alternative to the current production methods. Technologies of synthetic biology and metabolic engineering of microorganisms can be deployed for the expression of new biochemical pathways for MEG-synthesis from gases, provided that such promising alternative routes are first identified. We used the BNICE.ch algorithm to develop novel and previously unknown biological pathways to MEG from synthesis gas by leveraging the Wood-Ljungdahl pathway of carbon fixation of acetogenic bacteria. We developed a set of useful pathway pruning and analysis criteria to systematically assess thousands of pathways generated by BNICE.ch. Published genome-scale models of Moorella thermoacetica and Clostridium ljungdahlii were used to perform the pathway yield calculations and in-depth analyses of seven (7) newly developed biological MEG-producing pathways from gases, including CO₂, CO, and H₂. These analyses helped identify not only better candidate pathways, but also superior chassis organisms that can be used for metabolic engineering of the candidate pathways. The pathway generation, pruning, and detailed analysis procedures described in this study can also be used to develop biochemical pathways for other commodity chemicals from gaseous substrates.     

The signaling network of tumor invasion

The ability of a cell to invade its surroundings is an important hallmark of malignant tumors and results from aberrant cell signaling mechanisms. The signal transduction that leads to tumor invasion can be broken down into major pathways. Even though the pathway systems are distinct in themselves, none of these pathways operate independently when it comes to transmitting signals that culminate in an invasive phenotype. That is, the malignant change in one receptor not only leads to malignant changes directly downstream but can also affect the molecules of many other pathways. Three major pathway systems involved in tumor invasion are discussed in this review: the integrin system, the insulin-like growth factor system, and the Rho family GTPases. Here we see that although the individual signaling systems can each contribute to invasion, each system is networked to others and should not be considered isolated. Each system is first reviewed as independent contributors to an invasive phenotype and then discussed in the context of interacting pathways that collectively result in tumor invasion.     

Immune-To-Brain Signaling: How Important are the Blood–Brain Barrier-independent Pathways?

A conceptual obstacle for understanding immune-to-brain signaling is the issue of the blood-brain barrier (BBB). In the last 30 years, several pathways have been investigated to address the question of how peripheral immune signals are transmitted into the brain. These pathways can be categorized into two types: BBB-dependent pathways and BBB-independent pathways. BBB-dependent pathways involve the BBB as a relay station or porous barrier, whereas BBB-independent pathways use neuronal routes that bypass the BBB. Recently, a complete BBB-dependent ascending pathway for immune-to-brain signaling has been described. Details of BBB-independent pathways are still under construction. In this review, I will summarize the current progress in unraveling immune-to-brain signaling pathways. In addition, I will provide a critical analysis of the literature to point to areas where our knowledge of the immunological afferent signaling to the central nervous system is still sorely lacking.     

BioPAX support in CellDesigner

MOTIVATION: BioPAX is a standard language for representing and exchanging models of biological processes at the molecular and cellular levels. It is widely used by different pathway databases and genomics data analysis software. Currently, the primary source of BioPAX data is direct exports from the curated pathway databases. It is still uncommon for wet-lab biologists to share and exchange pathway knowledge using BioPAX. Instead, pathways are usually represented as informal diagrams in the literature. In order to encourage formal representation of pathways, we describe a software package that allows users to create pathway diagrams using CellDesigner, a user-friendly graphical pathway-editing tool and save the pathway data in BioPAX Level 3 format. AVAILABILITY: The plug-in is freely available and can be downloaded at ftp://ftp.pantherdb.org/CellDesigner/plugins/BioPAX/ CONTACT: huaiyumi@usc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

An algorithm for linear metabolic pathway alignment

Metabolic pathway alignment represents one of the most powerful tools for comparative analysis of metabolism. It involves recognition of metabolites common to a set of functionally-related metabolic pathways, interpretation of biological evolution processes and determination of alternative metabolic pathways. Moreover, it is of assistance in function prediction and metabolism modeling. Although research on genomic sequence alignment is extensive, the problem of aligning metabolic pathways has received less attention. We are motivated to develop an algorithm of metabolic pathway alignment to reveal the similarities between metabolic pathways. A new definition of the metabolic pathway is introduced. The algorithm has been implemented into the PathAligner system; its web-based interface is available at http://bibiserv.techfak.uni-bielefeld.de/pathaligner/.     

Topology-based cancer classification and related pathway mining using microarray data

Cancer classification is the critical basis for patient-tailored therapy, while pathway analysis is a promising method to discover the underlying molecular mechanisms related to cancer development by using microarray data. However, linking the molecular classification and pathway analysis with gene network approach has not been discussed yet. In this study, we developed a novel framework based on cancer class-specific gene networks for classification and pathway analysis. This framework involves a novel gene network construction, named ordering network, which exhibits the power-law node-degree distribution as seen in correlation networks. The results obtained from five public cancer datasets showed that the gene networks with ordering relationship are better than those with correlation relationship in terms of accuracy and stability of the classification performance. Furthermore, we integrated the ordering networks, classification information and pathway database to develop the topology-based pathway analysis for identifying cancer class-specific pathways, which might be essential in the biological significance of cancer. Our results suggest that the topology-based classification technology can precisely distinguish cancer subclasses and the topology-based pathway analysis can characterize the correspondent biochemical pathways even if there are subtle, but consistent, changes in gene expression, which may provide new insights into the underlying molecular mechanisms of tumorigenesis.     

Along signal paths: an empirical gene set approach exploiting pathway topology

Gene set analysis using biological pathways has become a widely used statistical approach for gene expression analysis. A biological pathway can be represented through a graph where genes and their interactions are, respectively, nodes and edges of the graph. From a biological point of view only some portions of a pathway are expected to be altered; however, few methods using pathway topology have been proposed and none of them tries to identify the signal paths, within a pathway, mostly involved in the biological problem. Here, we present a novel algorithm for pathway analysis clipper, that tries to fill in this gap. clipper implements a two-step empirical approach based on the exploitation of graph decomposition into a junction tree to reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it identifies within these pathways the signal paths having the greatest association with a specific phenotype. We test our approach on simulated and two real expression datasets. Our results demonstrate the efficacy of clipper in the identification of signal transduction paths totally coherent with the biological problem.     

基于途径分析的L-异亮氨酸发酵溶氧控制研究

利用途径分析方法对黄色短杆菌（Brevibacterium flavum）TC-21 生产L-异亮氨酸的途径进行了分析,确定了黄色短杆菌TC-21生产L-异亮氨酸的最佳途径的通量分布,根据途径分析的结果,TCA循环的代谢流量对L-异亮氨酸产量有明显影响,而TCA循环与发酵过程中的溶氧密切相关,因此可以通过控制溶氧来提高L-异亮氨酸产量。在发酵过程的不同阶段,根据菌体生长和产酸的需求,改变TCA代谢流量,可以有效提高产酸率。实验证明,通过溶氧分阶段控制发酵生产L-异亮氨酸,比溶氧恒定控制方式发酵产率提高了15.77％。实验结果说明,用途径分析的结果指导发酵过程中的溶氧可以大幅度提高L-异亮氨酸的产量。