Network methods for describing sample relationships in genomic datasets: application to Huntington’s disease

Background

We consider the possibility of engineering metabolic pathways in a chassis organism in order to synthesize novel target compounds that are heterologous to the chassis. For this purpose, we model metabolic networks through hypergraphs where reactions are represented by hyperarcs. Each hyperarc represents an enzyme-catalyzed reaction that transforms set of substrates compounds into product compounds. We follow a retrosynthetic approach in order to search in the metabolic space (hypergraphs) for pathways (hyperpaths) linking the target compounds to a source set of compounds.

Results

To select the best pathways to engineer, we have developed an objective function that computes the cost of inserting a heterologous pathway in a given chassis organism. In order to find minimum-cost pathways, we propose in this paper two methods based on steady state analysis and network topology that are to the best of our knowledge, the first to enumerate all possible heterologous pathways linking a target compounds to a source set of compounds. In the context of metabolic engineering, the source set is composed of all naturally produced chassis compounds (endogenuous chassis metabolites) and the target set can be any compound of the chemical space. We also provide an algorithm for identifying precursors which can be supplied to the growth media in order to increase the number of ways to synthesize specific target compounds.

Conclusions

We find the topological approach to be faster by several orders of magnitude than the steady state approach. Yet both methods are generally scalable in time with the number of pathways in the metabolic network. Therefore this work provides a powerful tool for pathway enumeration with direct application to biosynthetic pathway design.     

Exploring toxicity of perfluorinated compounds through complex network and pathway modeling

Abstract

Perfluorinated compounds (PFCs) have serious impacts on human health, which could interfere with the body's signal pathways and affect the normal hormone balance of humans. PFCs were reported to bind to many proteins causing a series of biological effects. It was quite possible that the in vivo action of PFCs was not a single target or a single pathway, suggesting the toxic effect was due to the disturbance of protein or gene network, not limited to the modification of a single target protein or gene. Thus, a PFCs-targets interaction network was constructed and the significant differences in the characteristics of complex networks between the branched PFCs and linear PFCs were observed. A molecular dynamics simulation proved that binding ability of the branched PFCs to the target protein was much weaker than that of the linear PFCs, explaining why the branched PFCs presented significantly difference from the linear PFCs in terms of complex network characteristics. In addition, four target genes were identified as the central node genes of the network. The four target genes were proved to present certain influences on some diseases, which suggested a high correlation between PFCs to these diseases, including obesity, hepatocellular carcinoma and diabetes. The present work was helpful to develop new approaches to identify the key toxic targets of compounds and to explore the toxicity effects on pathways. Abbreviations AR androgen receptor

BPA bisphenol A

ESR1 estrogen receptor 1

ESR2 estrogen receptor 2

GLTP glycolipid transfer protein

HbF the fetal hemoglobin

HBG1 hemoglobin subunit γ-1

hERα human ERα

HSD17B1 hydroxysteroid 17-β dehydrogenase 1

KEGG Kenya encyclopedia of genes and genomes

MD molecular dynamics simulation

PFCs perfluorinated compounds

PFOA perfluorooctanoic acid

PFOS perfluorooctane sulfonate

POPs persistent organic pollutants

RMSD root-mean-square deviation

SHBG sex hormone binding globulin

SPC/E extended simple point charge model

TR thyroid hormone receptor

Communicated by Ramaswamy H. Sarma     

Novel Inhibitors of Cholesterol Degradation in Mycobacterium tuberculosis Reveal How the Bacterium’s Metabolism Is Constrained by the Intracellular Environment

Mycobacterium tuberculosis (Mtb) relies on a specialized set of metabolic pathways to support growth in macrophages. By conducting an extensive, unbiased chemical screen to identify small molecules that inhibit Mtb metabolism within macrophages, we identified a significant number of novel compounds that limit Mtb growth in macrophages and in medium containing cholesterol as the principle carbon source. Based on this observation, we developed a chemical-rescue strategy to identify compounds that target metabolic enzymes involved in cholesterol metabolism. This approach identified two compounds that inhibit the HsaAB enzyme complex, which is required for complete degradation of the cholesterol A/B rings. The strategy also identified an inhibitor of PrpC, the 2-methylcitrate synthase, which is required for assimilation of cholesterol-derived propionyl-CoA into the TCA cycle. These chemical probes represent new classes of inhibitors with novel modes of action, and target metabolic pathways required to support growth of Mtb in its host cell. The screen also revealed a structurally-diverse set of compounds that target additional stage(s) of cholesterol utilization. Mutants resistant to this class of compounds are defective in the bacterial adenylate cyclase Rv1625/Cya. These data implicate cyclic-AMP (cAMP) in regulating cholesterol utilization in Mtb, and are consistent with published reports indicating that propionate metabolism is regulated by cAMP levels. Intriguingly, reversal of the cholesterol-dependent growth inhibition caused by this subset of compounds could be achieved by supplementing the media with acetate, but not with glucose, indicating that Mtb is subject to a unique form of metabolic constraint induced by the presence of cholesterol.     

Adipocytes under assault: Environmental disruption of adipose physiology

The burgeoning obesity epidemic has placed enormous strains on individual and societal health mandating a careful search for pathogenic factors, including the contributions made by endocrine disrupting chemicals (EDCs). In addition to evidence that some exogenous chemicals have the capacity to modulate classical hormonal signaling axes, there is mounting evidence that several EDCs can also disrupt metabolic pathways and alter energy homeostasis. Adipose tissue appears to be a particularly important target of these metabolic disruptions. A diverse array of compounds has been shown to alter adipocyte differentiation, and several EDCs have been shown to modulate adipocyte physiology, including adipocytic insulin action and adipokine secretion. This rapidly emerging evidence demonstrating that environmental contaminants alter adipocyte function emphasizes the potential role that disruption of adipose physiology by EDCs may play in the global epidemic of metabolic disease. Further work is required to better characterize the molecular targets responsible for mediating the effects of EDCs on adipose tissue. Improved understanding of the precise signaling pathways altered by exposure to environmental contaminants will enhance our understanding of which chemicals pose a threat to metabolic health and how those compounds synergize with lifestyle factors to promote obesity and its associated complications. This knowledge may also improve our capacity to predict which synthetic compounds may alter energy homeostasis before they are released into the environment while also providing critical evidentiary support for efforts to restrict the production and use of chemicals that pose the greatest threat to human metabolic health. This article is part of a Special Issue entitled: Modulation of Adipose Tissue in Health and Disease.     

Categorization of metabolome in bacterial systems

Analyses of biological databases such as those of genome, proteome, metabolome etc., have given insights in organization of biological systems. However, current efforts do not utilize the complete potential of available metabolome data. In this study, metabolome of bacterial systems with reliable annotations are analyzed and a simple method is developed to categorize pathways hierarchically, using rational approach. Ninety-four bacterial systems having for each ≥ 250 annotated metabolic pathways were used to identify a set of common pathways. 42 pathways were present in all bacteria which are termed as Core/Stage I pathways. This set of pathways was used along with interacting compounds to categorize pathways in the metabolome hierarchically. In each metabolome non-interacting pathways were identified including at each stage. The case study of Escherichia coli O157, having 433 annotated pathways, shows that 378 pathways interact directly or indirectly with 41 core pathways while 14 pathways are noninteracting. These 378 pathways are distributed in Stage II (289), Stage III (75), Stage IV (13) and Stage V (1) category. The approach discussed here allows understanding of the complexity of metabolic networks. It has pointed out that core pathways could be most ancient pathways and compounds that interact with maximum pathways may be compounds with high biosynthetic potential, which can be easily identified. Further, it was shown that interactions of pathways at various stages could be one to one, one to many, many to one or many to many mappings through interacting compounds. The granularity of the method discussed being high; the impact of perturbation in a pathway on the metabolome and particularly sub networks can be studied precisely. The categorizations of metabolic pathways help in identifying choke point enzymes that are useful to identify probable drug targets. The Metabolic categorizations for 94 bacteria are available at http://115.111.37.202/mpe/.     

A computational framework for the topological analysis and targeted disruption of signal transduction networks

In this article, optimization-based frameworks are introduced for elucidating the input-output structure of signaling networks and for pinpointing targeted disruptions leading to the silencing of undesirable outputs in therapeutic interventions. The frameworks are demonstrated on a large-scale reconstruction of a signaling network composed of nine signaling pathways implicated in prostate cancer. The Min-Input framework is used to exhaustively identify all input-output connections implied by the signaling network structure. Results reveal that there exist two distinct types of outputs in the signaling network that either can be elicited by many different input combinations or are highly specific requiring dedicated inputs. The Min-Interference framework is next used to precisely pinpoint key disruptions that negate undesirable outputs while leaving unaffected necessary ones. In addition to identifying disruptions of terminal steps, we also identify complex disruption combinations in upstream pathways that indirectly negate the targeted output by propagating their action through the signaling cascades. By comparing the obtained disruption targets with lists of drug molecules we find that many of these targets can be acted upon by existing drug compounds, whereas the remaining ones point at so-far unexplored targets. Overall the proposed computational frameworks can help elucidate input/output relationships of signaling networks and help to guide the systematic design of interference strategies.     

Matrix method for determining steps most rate-limiting to metabolic fluxes in biotechnological processes

The metabolic control theory developed by Kacser, Burns, Heinrich, and Rapoport is briefly outlined, extended, and transformed so as optimally to address some biotechnological questions. The extensions include (i) a new theorem that relates the control of metabolite concentrations by enzyme activities to flux ratios at branches in metabolic pathways; (ii) a new theorem that does the same for the control of the distribution of the flux over two branches; (iii) a method that expresses these controls into properties (the so-called elasticity coefficients) of the enzymes in the pathway; and (iv) a theorem that relates the effects of changes in metabolite concentrations on reaction rates to the effects of changes in enzyme properties on the same rates. Matrix equations relating the flux control and concentration control coefficients to the elasticity coefficients of enzymes in simple linear and branched pathways incorporating feedback are given, together with their general solutions and a numerical example. These equations allow one to develop rigorous criteria by which to decide the optimal strategy for the improvement of a microbial process. We show how this could be used in deciding which property of which enzyme should be changed in order to obtain the maximal concentration of a metabolite or the maximal metabolic flux.     

Cross-talk in plant hormone signalling: what Arabidopsis mutants are telling us

Genetic screens have been extremely useful in identifying genes involved in hormone signal transduction. However, although these screens were originally designed to identify specific components involved in early hormone signalling, mutations in these genes often confer changes in sensitivity to more than one hormone at the whole-plant level. Moreover, a variety of hormone response genes has been identified through screens that were originally designed to uncover regulators of sugar metabolism. Together, these facts indicate that the linear representation of the hormone signalling pathways controlling a specific aspect of plant growth and development is not sufficient, and that hormones interact with each other and with a variety of developmental and metabolic signals. Following the advent of arabidopsis molecular genetics we are beginning to understand some of the mechanisms by which a hormone is transduced into a cellular response. In this Botanical Briefing we review a subset of genes in arabidopsis that influence hormonal cross-talk, with emphasis on the gibberellin, abscisic acid and ethylene pathways. In some cases it appears that modulation of hormone sensitivity can cause changes in the synthesis of an unrelated hormone, while in other cases a hormone response gene defines a node of interaction between two response pathways. It also appears that a variety of hormones may converge to regulate the turnover of important regulators involved in growth and development. Examples are cited of the recent use of suppressor and enhancer analysis to identify new nodes of interaction between these pathways.     

Linear topology confers in vivo gene transfer activity to polyethylenimines

Although polyethylenimines (PEIs) are frequently used transfection agents, it is still unclear which of their properties are required for efficient gene delivery. This is even more striking when working in vivo since some PEIs are able to generate significant gene expression, whereas others are not. To facilitate a rational development of compounds with improved transfection activities, studies aimed at identifying the properties involved in the transfection process seem indispensable. In the present work, we investigated how transfection with linear PEI of 22 kDa allows for high reporter gene expression in lungs after intravenous injection, whereas the branched PEI of 25 kDa does not. To this end, we synthesized L-PEI derivatives that are intermediates between linear and branched PEIs. Our results show that the topology plays a crucial role in obtaining in vivo reporter gene expression, whereas the content of primary, secondary, and tertiary amines is only of minor importance.     

A graph layout algorithm for drawing metabolic pathways

MOTIVATION: A large amount of data on metabolic pathways is available in databases. The ability to visualise the complex data dynamically would be useful for building more powerful research tools to access the databases. Metabolic pathways are typically modelled as graphs in which nodes represent chemical compounds, and edges represent chemical reactions between compounds. Thus, the problem of visualising pathways can be formulated as a graph layout problem. Currently available visual interfaces to biochemical databases either use static images or cannot cope well with more complex, non-standard pathways. RESULTS: This paper presents a new algorithm for drawing pathways which uses a combination of circular, hierarchic and force-directed graph layout algorithms to compute positions of the graph elements representing main compounds and reactions. The algorithm is particularly designed for cyclic or partially cyclic pathways or for combinations of complex pathways. It has been tested on five sample pathways with promising results.     

Selection for novel metabolic capabilities in Salmonella enterica

Bacteria are known to display extensive metabolic diversity and many studies have shown that they can use an extensive repertoire of small molecules as carbon‐ and energy sources. However, it is less clear to what extent a bacterium can expand its existing metabolic capabilities by acquiring mutations that, for example, rewire its metabolic pathways. To investigate this capability and potential for evolution of novel phenotypes, we sampled large populations of mutagenized Salmonella enterica to select very rare mutants that can grow on minimal media containing 124 low molecular weight compounds as sole carbon sources. We found mutants growing on 18 of these novel carbon sources, and identified the causal mutations that allowed growth for four of them. Mutations that relieve physiological constraints or increase expression of existing pathways were found to be important contributors to the novel phenotypes. For the remaining 14 novel phenotypes, whole genome sequencing of independent mutants and genetic analysis suggested that these novel metabolic phenotypes result from a combination of multiple mutations. This work, by virtue of identifying the genetic and mechanistic basis for new metabolic capabilities, sheds light on the properties of adaptive landscapes underlying the evolution of novel phenotypes.     

Synthetic Pathway for Production of Five-Carbon Alcohols from Isopentenyl Diphosphate

Synthetic biological pathways could enhance the development of novel processes to produce chemicals from renewable resources. On the basis of models that describe the evolution of metabolic pathways and enzymes in nature, we developed a framework to rationally identify enzymes able to catalyze reactions on new substrates that overcomes one of the major bottlenecks in the assembly of a synthetic biological pathway. We verified the framework by implementing a pathway with two novel enzymatic reactions to convert isopentenyl diphosphate into 3-methyl-3-butenol, 3-methyl-2-butenol, and 3-methylbutanol. To overcome competition with native pathways that share the same substrate, we engineered two bifunctional enzymes that redirect metabolic flux toward the synthetic pathway. Taken together, our work demonstrates a new approach to the engineering of novel synthetic pathways in the cell.     

Expression-level optimization of a multi-enzyme pathway in the absence of a high-throughput assay

Engineered metabolic pathways often suffer from flux imbalances that can overburden the cell and accumulate intermediate metabolites, resulting in reduced product titers. One way to alleviate such imbalances is to adjust the expression levels of the constituent enzymes using a combinatorial expression library. Typically, this approach requires high-throughput assays, which are unfortunately unavailable for the vast majority of desirable target compounds. To address this, we applied regression modeling to enable expression optimization using only a small number of measurements. We characterized a set of constitutive promoters in Saccharomyces cerevisiae that spanned a wide range of expression and maintained their relative strengths irrespective of the coding sequence. We used a standardized assembly strategy to construct a combinatorial library and express for the first time in yeast the five-enzyme violacein biosynthetic pathway. We trained a regression model on a random sample comprising 3% of the total library, and then used that model to predict genotypes that would preferentially produce each of the products in this highly branched pathway. This generalizable method should prove useful in engineering new pathways for the sustainable production of small molecules.     

Fundamental Escherichia coli biochemical pathways for biomass and energy production: identification of reactions

Cells grow by oxidizing nutrients using a complex network of biochemical reactions. During this process new biological material is produced along with energy used for maintaining cellular organization. Because the metabolic network is highly branched, these tasks can be accomplished using a wide variety of unique reaction sequences. However, evolutionary pressures under carbon-limited growth conditions likely select organisms that utilize highly efficient pathways. Using elementary-mode analysis, we demonstrate that the metabolism of the bacterium Escherichia coli contains four unique pathways that most efficiently convert glucose and oxygen into new cells and maintenance energy under any level of oxygen limitation. Observed regulatory patterns and experimental findings suggest growing cells use these highly efficient pathways. It is predicted that five knockout mutations generate a strain that supports growth using only the most efficient reaction sequence. The analysis approach should be generally useful for predicting metabolic capabilities and efficient network designs based on only genomic information.     

Use of implicit methods from general sensitivity theory to develop a systematic approach to metabolic control. II. Complex systems

In the accompanying paper (Cascante et al., this issue) we have used general sensitivity theory to develop a matrix algebra that, in the case of sequential reactions, directly relates global and local properties of a given system. In complex biochemical systems this direct relationship is not possible due to the existence of linear dependencies among fluxes and among metabolite concentrations (conserved aggregate concentrations in BST or moiety-conserved concentrations in MCT). In this paper our matrix algebra is applied to conserved cycles and branched pathways, and it is shown that with minor modifications it again relates global properties to the local properties of the enzymes in the system. In the case of conserved cycles, elasticities become modified due to the existence of linear dependencies among the concentration variables in the cycle. In branched pathways, new matrix elements involving ratios of fluxes appear. With these modifications, one can show that the so-called theorems of metabolic control theory specific to these types of pathways are special cases of more general relationships. Rules for the construction of matrices relating global and local properties are given that apply to an arbitrary system of cycles and branches. The implicit approach developed in these papers, which is a generalization of that used in MCT, allows one to make more direct comparisons with the general explicit approach originally developed in BST.