Construction and Analyses of Human Large-Scale Tissue Specific Networks

Construction and analyses of tissue specific networks is crucial to unveil the function and organizational structure of biological systems. As a direct method to detect protein dynamics, human proteome-wide expression data provide an valuable resource to investigate the tissue specificity of proteins and interactions. By integrating protein expression data with large-scale interaction network, we constructed 30 tissue/cell specific networks in human and analyzed their properties and functions. Rather than the tissue specificity of proteins, we mainly focused on the tissue specificity of interactions to distill tissue specific networks. Through comparing our tissue specific networks with those inferred from gene expression data, we found our networks have larger scales and higher reliability. Furthermore, we investigated the similar extent of multiple tissue specific networks, which proved that tissues with similar functions tend to contain more common interactions. Finally, we found that the tissue specific networks differed from the static network in multiple topological properties. The proteins in tissue specific networks are interacting looser and the hubs play more important roles than those in the static network.     

Tissue specificity of mitochondrial glutamate pathways and the control of metabolic homeostasis

Glutamate is implicated in numerous metabolic and signalling functions that vary according to specific tissues. Glutamate metabolism is tightly controlled by activities of mitochondrial enzymes and transmembrane carriers, in particular glutamate dehydrogenase and mitochondrial glutamate carriers that have been identified in recent years. It is remarkable that, although glutamate-specific enzymes and transporters share similar properties in most tissues, their regulation varies greatly according to particular organs in order to achieve tissue specific functions. This is illustrated in this review when comparing glutamate handling in liver, brain, and pancreatic beta-cells. We describe the main cellular glutamate pathways and their specific functions in different tissues, ultimately contributing to the control of metabolic homeostasis at the organism level.     

Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks

Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable.     

A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory

Background

The study of biological interaction networks is a central theme of systems biology. Here, we investigate the relationships between two distinct types of interaction networks: the metabolic pathway map and the protein-protein interaction network (PIN). It has long been established that successive enzymatic steps are often catalyzed by physically interacting proteins forming permanent or transient multi-enzymes complexes. Inspecting high-throughput PIN data, it was shown recently that, indeed, enzymes involved in successive reactions are generally more likely to interact than other protein pairs. In our study, we expanded this line of research to include comparisons of the underlying respective network topologies as well as to investigate whether the spatial organization of enzyme interactions correlates with metabolic efficiency.

Results

Analyzing yeast data, we detected long-range correlations between shortest paths between proteins in both network types suggesting a mutual correspondence of both network architectures. We discovered that the organizing principles of physical interactions between metabolic enzymes differ from the general PIN of all proteins. While physical interactions between proteins are generally dissortative, enzyme interactions were observed to be assortative. Thus, enzymes frequently interact with other enzymes of similar rather than different degree. Enzymes carrying high flux loads are more likely to physically interact than enzymes with lower metabolic throughput. In particular, enzymes associated with catabolic pathways as well as enzymes involved in the biosynthesis of complex molecules were found to exhibit high degrees of physical clustering. Single proteins were identified that connect major components of the cellular metabolism and may thus be essential for the structural integrity of several biosynthetic systems.

Conclusion

Our results reveal topological equivalences between the protein interaction network and the metabolic pathway network. Evolved protein interactions may contribute significantly towards increasing the efficiency of metabolic processes by permitting higher metabolic fluxes. Thus, our results shed further light on the unifying principles shaping the evolution of both the functional (metabolic) as well as the physical interaction network.     

Metabolic networks are NP-hard to reconstruct

High-throughput data from various omics and sequencing techniques have rendered the automated metabolic network reconstruction a highly relevant problem. Our approach reflects the inherent probabilistic nature of the steps involved in metabolic network reconstruction. Here, the goal is to arrive at networks which combine probabilistic information with the possibility to obtain a small number of disconnected network constituents by reduction of a given preliminary probabilistic metabolic network. We define automated metabolic network reconstruction as an optimization problem on four-partite graph (nodes representing genes, enzymes, reactions, and metabolites) which integrates: (1) probabilistic information obtained from the existing process for metabolic reconstruction from a given genome, (2) connectedness of the raw metabolic network, and (3) clustering of components in the reconstructed metabolic network. The practical implications of our theoretical analysis refer to the quality of reconstructed metabolic networks and shed light on the problem of finding more efficient and effective methods for automated reconstruction. Our main contributions include: a completeness result for the defined problem, polynomial-time approximation algorithm, and an optimal polynomial-time algorithm for trees. Moreover, we exemplify our approach by the reconstruction of the sucrose biosynthesis pathway in Chlamydomonas reinhardtii.     

The evolution of connectivity in metabolic networks

Processes in living cells are the result of interactions between biochemical compounds in highly complex biochemical networks. It is a major challenge in biology to understand causes and consequences of the specific design of these networks. A characteristic design feature of metabolic networks is the presence of hub metabolites such as ATP or NADH that are involved in a high number of reactions. To study the emergence of hub metabolites, we implemented computer simulations of a widely accepted scenario for the evolution of metabolic networks. Our simulations indicate that metabolic networks with a large number of highly specialized enzymes may evolve from a few multifunctional enzymes. During this process, enzymes duplicate and specialize, leading to a loss of biochemical reactions and intermediary metabolites. Complex features of metabolic networks such as the presence of hubs may result from selection of growth rate if essential biochemical mechanisms are considered. Specifically, our simulations indicate that group transfer reactions are essential for the emergence of hubs.     

Integrated analysis of protein composition, tissue diversity, and gene regulation in mouse mitochondria

Mitochondria are tailored to meet the metabolic and signaling needs of each cell. To explore its molecular composition, we performed a proteomic survey of mitochondria from mouse brain, heart, kidney, and liver and combined the results with existing gene annotations to produce a list of 591 mitochondrial proteins, including 163 proteins not previously associated with this organelle. The protein expression data were largely concordant with large-scale surveys of RNA abundance and both measures indicate tissue-specific differences in organelle composition. RNA expression profiles across tissues revealed networks of mitochondrial genes that share functional and regulatory mechanisms. We also determined a larger "neighborhood" of genes whose expression is closely correlated to the mitochondrial genes. The combined analysis identifies specific genes of biological interest, such as candidates for mtDNA repair enzymes, offers new insights into the biogenesis and ancestry of mammalian mitochondria, and provides a framework for understanding the organelle's contribution to human disease.     

The human extended mitochondrial metabolic network: new hubs from lipids

Even if systems thinking is not new in biology, rationalizing the explosively growing amount of knowledge has been the compelling reason for the sudden rise and spreading of systems biology. Based on 'omics' data, several genome-scale metabolic networks have been reconstructed and validated. One of the most striking aspects of complex metabolic networks is the pervasive power-law appearance of metabolite connectivity. However, the combinatorial diversity of some classes of compounds, such as lipids, has been scarcely considered so far. In this work, a lipid-extended human mitochondrial metabolic network has been built and analyzed. It is shown that, considering combinatorial diversity of lipids and multipurpose enzymes, an intimate connection between membrane lipids and oxidative phosphorilation appears. This finding leads to some biomedical considerations on diseases involving mitochondrial enzymes. Moreover, the lipid-extended network still shows power-law features. Power-law distributions are intrinsic to metabolic network organization and evolution. Hubs in the lipid-extended mitochondrial network strongly suggest that the "RNA world" and the "lipid world" hypothesis are both correct.     

Profiling the metabolic proteome of bovine mammary tissue

2-DE and MALDI mass fingerprinting were used to analyse mammary tissue from lactating Friesian cows. The goal was detection of enzymes in metabolic pathways for synthesis of milk molecules including fatty acids and lactose. Of 418 protein spots analysed by PMF, 328 were matched to database sequences, resulting in 215 unique proteins. We detected 11 out of the 15 enzymes in the direct pathways for conversion of glucose to fatty acids, two of the pentose phosphate pathway enzymes and two of the enzymes for lactose synthesis from glucose. We did not detect enzymes that catalyse the first three reactions of glycolysis. Our results are typical of enzyme detection using 2-DE of mammalian tissues. We therefore advocate caution when relating enzyme abundances measured by 2-DE to metabolic output as not all relevant proteins are detected. 2-D DIGE was used to measure interindividual variation in enzyme abundance from eight animals. We extracted relative protein abundances from 2-D DIGE data and used a logratio transformation that is appropriate for compositional data of the kind represented in many proteomics experiments. Coefficients of variation for abundances of detected enzymes were 3-8%. We recommend use of this transformation for DIGE and other compositional data.     

Patterns of indirect protein interactions suggest a spatial organization to metabolism

It has long been believed that cells organize their cytoplasm so as to efficiently channel metabolites between sequential enzymes. This metabolic channeling has the potential to yield higher metabolic fluxes as well as better regulatory control over metabolism. One mechanism for achieving such channeling is to ensure that sequential enzymes in a pathway are physically close to each other in the cell. We present evidence that indirect protein interactions between related enzymes represent a global mechanism for achieving metabolic channeling; the intuition being that protein interactions between enzymes and non-enzymatic mediator proteins are a powerful means of physically associating enzymes in a modular fashion. By analyzing the metabolic and protein-protein interactions networks of Escherichia coli, yeast and humans, we are able to show that all three species have many more indirect protein interactions linking enzymes that share metabolites than would be expected by chance. Moreover, these interactions are distributed non-randomly in the metabolic network. Our analyses in yeast and E. coli show that reactions possessing such interactions also show higher flux than do those lacking them. On the basis of these observations, we suggest that an important role of protein interactions with mediator proteins is to contribute to the spatial organization of the cell. This hypothesis is supported by the fact that these mediator proteins are also enriched with annotations related to signal transduction, a system where scaffolding proteins are known to limit cross-talk by controlling spatial localization.     

Observing local and global properties of metabolic pathways: 'load points' and 'choke points' in the metabolic networks

MOTIVATION: The local and global aspects of metabolic network analyses allow us to identify enzymes or reactions that are crucial for the survival of the organism(s), therefore directing us towards the discovery of potential drug targets. RESULTS: We demonstrate a new method ('load points') to rank the enzymes/metabolites in the metabolic network and propose a model to determine and rank the biochemical lethality in metabolic networks (enzymes/metabolites) through 'choke points'. Based on an extended form of the graph theory model of metabolic networks, metabolite structural information was used to calculate the k-shortest paths between metabolites (the presence of more than one competing path between substrate and product). On the basis of these paths and connectivity information, load points were calculated and used to empirically rank the importance of metabolites/enzymes in the metabolic network. The load point analysis emphasizes the role that the biochemical structure of a metabolite, rather than its connectivity (hubs), plays in the conversion pathway. In order to identify potential drug targets (based on the biochemical lethality of metabolic networks), the concept of choke points and load points was used to find enzymes (edges) which uniquely consume or produce a particular metabolite (nodes). A non-pathogenic bacterial strain Bacillus subtilis 168 (lactic acid producing bacteria) and a related pathogenic bacterial strain Bacillus anthracis Sterne (avirulent but toxigenic strain, producing the toxin Anthrax) were selected as model organisms. The choke point strategy was implemented on the pathogen bacterial network of B.anthracis Sterne. Potential drug targets are proposed based on the analysis of the top 10 choke points in the bacterial network. A comparative study between the reported top 10 bacterial choke points and the human metabolic network was performed. Further biological inferences were made on results obtained by performing a homology search against the human genome. AVAILABILITY: The load and choke point modules are introduced in the Pathway Hunter Tool (PHT), the basic version of which is available on http://www.pht.uni-koeln.de.     

Evolutionary constraints permeate large metabolic networks

Background  

Metabolic networks show great evolutionary plasticity, because they can differ substantially even among closely related prokaryotes. Any one metabolic network can also effectively compensate for the blockage of individual reactions by rerouting metabolic flux through other pathways. These observations, together with the continual discovery of new microbial metabolic pathways and enzymes, raise the possibility that metabolic networks are only weakly constrained in changing their complement of enzymatic reactions.     

Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks

Background  

Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure.     

Identification and characterization of protein composition in Withania somnifera—an Indian ginseng

To have better understanding of the processes that occur in Withania somnifera L. Dunal, proteome analyses were initiated on two tissues (seeds & leaves) of this plant. Protein extracts were separated by two-dimensional gel electrophoresis (2-DE) across a broad 3.0?C10.0 immobilized pH gradient (IPG) strip that yielded 434 protein spots. A total of 167 individual spots (82 from seeds and 85 from leaves) were excised from the gel and were characterized by peptide mass fingerprinting. From these analyses, 70 individual proteins from seeds and 74 from leaves were identified by protein sequence database interrogation and were catalogued accordingly to different protein functions. A comparative analysis of the two tissues indicated that some enzymes/proteins involved in housekeeping pathways were common to both, whereas some were exclusively tissue specific with specialized metabolic complement. The knowledge gained by this study towards the tissue specific protein expression in W. somnifera would form the basis for our future endeavor of characterization of proteins to understand the physiology and the associated complex metabolic network during its ontogenetic development.     

The Activity of Class I, II, III and IV of Alcohol Dehydrogenase (ADH) Isoenzymes and Aldehyde Dehydrogenase (ALDH) in Brain Cancer

The brain being highly sensitive to the action of alcohol is potentially susceptible to its carcinogenic effects. Alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) are the main enzymes involved in ethanol metabolism, which leads to the generation of carcinogenic acetaldehyde. Human brain tissue contains various ADH isoenzymes and possess also ALDH activity. The purpose of this study was to compare the capacity for ethanol metabolism measured by ADH isoenzymes and ALDH activity in cancer tissues and healthy brain cells. The samples were taken from 62 brain cancer patients (36 glioblastoma, 26 meningioma). For the measurement of the activity of class I and II ADH isoenzymes and ALDH activity, the fluorometric methods were used. The total ADH activity and activity of class III and IV isoenzymes were measured by the photometric method. The total activity of ADH, and activity of class I ADH were significantly higher in cancer cells than in healthy tissues. The other tested classes of ADH and ALDH did not show statistically significant differences of activity in cancer and in normal cells. Analysis of the enzymes activity did not show significant differences depending on the location of the tumor. The differences in the activity of total alcohol dehydrogenase, and class I isoenzyme between cancer tissues and healthy brain cells might be a factor for metabolic changes and disturbances in low mature cancer cells and additionally might be a reason for higher level of acetaldehyde which can intensify the carcinogenesis.     

The nature of the dietary protein impacts the tissue-to-diet 15N discrimination factors in laboratory rats

Due to the existence of isotope effects on some metabolic pathways of amino acid and protein metabolism, animal tissues are (15)N-enriched relative to their dietary nitrogen sources and this (15)N enrichment varies among different tissues and metabolic pools. The magnitude of the tissue-to-diet discrimination (Δ(15)N) has also been shown to depend on dietary factors. Since dietary protein sources affect amino acid and protein metabolism, we hypothesized that they would impact this discrimination factor, with selective effects at the tissue level. To test this hypothesis, we investigated in rats the influence of a milk or soy protein-based diet on Δ(15)N in various nitrogen fractions (urea, protein and non-protein fractions) of blood and tissues, focusing on visceral tissues. Regardless of the diet, the different protein fractions of blood and tissues were generally (15)N-enriched relative to their non-protein fraction and to the diet (Δ(15)N>0), with large variations in the Δ(15)N between tissue proteins. Δ(15)N values were markedly lower in tissue proteins of rats fed milk proteins compared to those fed soy proteins, in all sampled tissues except in the intestine, and the amplitude of Δ(15)N differences between diets differed between tissues. Both between-tissue and between-diet Δ(15)N differences are probably related to modulations of the relative orientation of dietary and endogenous amino acids in the different metabolic pathways. More specifically, the smaller Δ(15)N values observed in tissue proteins with milk than soy dietary protein may be due to a slightly more direct channeling of dietary amino acids for tissue protein renewal and to a lower recycling of amino acids through fractionating pathways. In conclusion, the present data indicate that natural Δ(15)N of tissue are sensitive markers of the specific subtle regional modifications of the protein and amino acid metabolism induced by the protein dietary source.